Learning Plan Networks in Conversational Video

Transcription

Learning Plan Networks in
Conversational Video Games
by
Jeffrey David Orkin
B.S., Tufts University (1995)
M.S., University of Washington (2003)
Submitted to the Program in Media Arts and Sciences
in partial fulfillment of the requirements for the degree of
Master of Science
at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
August 2007
© Massachusetts Institute of Technology 2007. All rights reserved.
Author . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Program in Media Arts and Sciences
August 13, 2007
Certified by. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Deb Roy
Associate Professor
Thesis Supervisor
Accepted by . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Deb Roy
Chairperson, Departmental Committee on Graduate Students
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by
Jeffrey David Orkin
on August 13, 2007, in partial fulfillment of the
requirements for the degree of
Master of Science
Abstract
We look forward to a future where robots collaborate with humans in the home and
workplace, and virtual agents collaborate with humans in games and training simulations.
A representation of common ground for everyday scenarios is essential for these agents if
they are to be effective collaborators and communicators. Effective collaborators can
infer a partner’s goals and predict future actions. Effective communicators can infer the
meaning of utterances based on semantic context. This thesis introduces a computational
cognitive model of common ground called a Plan Network. A Plan Network is a
statistical model that provides representations of social roles, object affordances, and
expected patterns of behavior and language. I describe a methodology for unsupervised
learning of a Plan Network using a multiplayer video game, visualization of this network,
and evaluation of the learned model with respect to human judgment of typical behavior.
Specifically, I describe learning the Restaurant Plan Network from data collected from
over 5,000 players of an online game called The Restaurant Game.
Thesis Supervisor: Deb Roy
Title: Associate Professor
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4
by
Jeffrey David Orkin
in partial fulfillment of the requirements for the Master of Science
at the
August 2007
Advisor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Deb Roy
Associate Professor of Media Arts and Sciences
MIT Media Lab
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by
Jeffrey David Orkin
at the
August 2007
Thesis Reader . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Cynthia Breazeal
Associate Professor of Media Arts and Sciences
MIT Media Lab
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by
Jeffrey David Orkin
at the
August 2007
Thesis Reader . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Henry Lieberman
Research Scientist
MIT Media Lab
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by
Jeffrey David Orkin
at the
August 2007
Thesis Reader . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Will Wright
Chief Game Designer
Maxis, Electronic Arts
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Acknowledgements
A large scale data collection effort like this cannot be accomplished alone. I owe thanks to
literally thousands of people; some of whom I know, and some of whom I do not. I thank
everyone that played The Restaurant Game; my friends Chris Darken, Brad Pendleton, David
Wenger, and Jason Greenberg for beta testing from the west coast; Paul Tozour for his early
support and feedback, and for playing 23 times; Andrea Thomaz and Steve Rabin for spreading
the word at HRI and GDC; Dan Robbins for his user interface insights; my Thesis Readers
Professor Cynthia Breazeal, Henry Lieberman, and Will Wright for their feedback, and for
inspiring me with their own work; Professor Deb Roy for broadening my horizons, giving me the
freedom to explore, and the guidance to succeed; the Cognitive Machines Group, especially
Michael Fleischman for many lessons in computational linguistics, Stefanie Tellex for evaluation
guidance, and Peter Gorniak for shaping my thinking about collaborative planning through early
prototyping; my parents for their support and enthusiasm for my education; and most of all my
wife Melissa for playing the customer in two of the first three games on launch day, and for
supporting and encouraging me in every aspect of my life.
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Contents
1 Introduction......................................................................... 21
1.1
1.2
1.3
1.4
Motivation......................................................................................................................22
Learning Plan Networks .................................................................................................23
Evaluating Plan Networks ..............................................................................................24
Outline of the Thesis ......................................................................................................25
2 Related Work....................................................................... 26
2.1
2.2
2.3
2.4
Cognitive Psychology.....................................................................................................26
Chatbots and Video Games.............................................................................................27
Language Understanding ................................................................................................28
Learning Plans................................................................................................................28
3 Data Collection with The Restaurant Game ..................... 30
3.1 What is The Restaurant Game?.......................................................................................30
3.2 Development of The Restaurant Game ...........................................................................31
3.3 Design Considerations for The Restaurant Game............................................................32
3.3.1 Accessibility to a Wide Audience ............................................................................32
3.3.2 Player Retention ......................................................................................................33
3.3.3 Natural Conversation ...............................................................................................34
3.3.4 Freedom for Dramatic Role-Playing ........................................................................35
3.4 Where Does Data Come From? ......................................................................................36
3.4.1 Rallying the Masses.................................................................................................36
3.4.2 Player Demographics...............................................................................................39
3.5 Lessons Learned About Game-Based Data Collection ....................................................41
3.5.1 Lessons Learned About Publicity.............................................................................42
3.5.2 Lessons Learned About Third Party Technology......................................................43
3.5.3 Old Habits Die Hard ................................................................................................43
4 Building and Visualizing Plan Networks........................... 44
4.1 Visualizing Plan Networks .............................................................................................47
4.1.1 Graphing Physical Actions.......................................................................................47
4.1.2 Browsing Conversations ..........................................................................................60
4.2 Building Plan Networks..................................................................................................63
4.2.1 Terminology and Representation .............................................................................64
4.2.2 Building an Action Lexicon .....................................................................................65
4.2.3 Clustering Actions ...................................................................................................66
4.2.4 Building a Language Lexicon ..................................................................................67
4.2.5 N-gram Models of Language and Behavior..............................................................68
5 Evaluation, Results, and Discussion................................... 71
5.1 Tuning the System for Optimal Correlation ....................................................................71
5.2 Testing the System .........................................................................................................81
5.2.1 Inter-Rater Agreement .............................................................................................82
5.2.2 Test Results .............................................................................................................84
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5.3 System Successes and Failures .......................................................................................86
6 Contributions and Future Work........................................ 94
A Clusters................................................................................ 96
B Maximum Action Lexicon Size .......................................... 98
B.1 Unclustered Actions.......................................................................................................98
B.2 Clustered Actions ..........................................................................................................99
C Learned Language Lexicon ............................................. 100
Bibliography ......................................................................... 121
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List of Figures
Figure 1-1: The Restaurant Game was developed with the Torque game engine, and
content from The Sims 2……………………………………………………. 22
Figure 3-1: Vague objectives for the waitress…………………………………………….
Figure 3-2: Vague objectives for the customer……………………………………………
Figure 3-3: Interface for interacting with objects…………………………………………
Figure 3-4: Post-game survey…………………………………………………………….
Figure 3-5: Games completed per week………………………………………………….
Figure 3-6: Project web page hits per week……………………………………………....
Figure 3-7: Where players heard about The Restaurant Game…………………………..
Figure 3-8: Games played per platform…………………………………………………..
Figure 3-9: Geographic distribution of players as reported by Google Analytics………..
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Figure 4-1: A raw log file from a gameplay session……………………………………...
Figure 4-2: A filtered script, generated from a raw log file………………………………
Figure 4-3: Graph visualization of one gameplay session………………………………..
Figure 4-4: Graph visualization of a second game……………………………………….
Figure 4-5: Graph visualization merging two games…………………………………….
Figure 4-6: Graph visualization merging 5,000 games…………………………………..
Figure 4-7: Zoomed-in portion of graph visualization merging 5,000 games…………...
Figure 4-8: Filtered graph of only waitress behavior from 5,000 games………………...
Figure 4-9: Filtered graph of only waitress behavior from 5,000 games, with colorcoded portions that will be expanded in the following figures…………….
Figure 4-10: Beginning of game for waitress, as learned from 5,000 games……………
Figure 4-11: Decision point for waitress, as learned from 5,000 games………………...
Figure 4-12: End of game for waitress, as learned from 5,000 games…………………..
Figure 4-13: Browsing conversations after picking up food, captured from 5,000 games.
Figure 4-14: Browsing conversations after putting down food, captured from
5,000 games………………………………………………………………..
Figure 4-15: Browsing conversations after picking up fruit bowl, captured from
5,000 games………………………………………………………………..
Figure 4-16: Growth of action lexicon over 5,000 games……………………………….
Figure 4-17: Growth of action lexicon, clustered and unclustered, over 5,000 games….
Figure 4-18: Growth of language lexicon over 5,000 games……………………………
Figure 4-19: Katz back-off model for bigrams………………………………………….
Figure 4-20: Katz back-off model for trigrams………………………………………….
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Figure 5-1: Histogram of human ratings for validation set………………………………
Figure 5-2: Effect of discount factor on correlation……………………………………...
Figure 5-3: Correlation between action model likelihoods and human ratings…………..
Figure 5-4: Top 20 waitress 4-gram plan fragments……………………………………..
Figure 5-5: Top 20 customer 4-gram plan fragments…………………………………….
Figure 5-6: Correlation between language model likelihoods and human ratings……….
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Figure 5-7: Correlation between n-gram model likelihoods and human ratings…………
Figure 5-8: Correlation between interpolated likelihoods combining actions and
language and human ratings………………………………………………..
Figure 5-9: Effect of interpolating action and language models on correlation for
the validation set……………………………………………………………
Figure 5-10: Scatter plot of correlation between likelihoods and human ratings………...
Figure 5-11: Human inter-rater agreement……………………………………………….
Figure 5-12: Rater agreement with my ratings, based on mode of 10 rater’s ratings…….
Figure 5-13: Histogram of human ratings for test set…………………………………….
Figure 5-14: Scatter plot of correlation between likelihoods and human ratings………...
Figure 5-15: Effect of interpolating action and language models on correlation for
the test set…………………………………………………………………...
Figure 5-16: Graph of customer behavior in a game rated typical by humans and
the system…………………………………………………………………...
Figure 5-17: Game with typical physical behavior, but atypical language……………….
Figure 5-18: Graph of customer behavior in a game rated atypical by humans and
the system…………………………………………………………………....
Figure 5-19: Waitress and customer order lots of pie and beer…………………………...
Figure 5-20: Complete script for a very short game………………………………………
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List of Tables
Table 3-1: Gameplay statistics from 5,200 games……………………………………….
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Table 4-1: Game length statistics, based on 5,000 games……………………………….. 65
Table 4-2: Speech act statistics, based on 5,000 games…………………………………. 68
Table 5-1: Descriptions of typicality ratings given to raters……………………………..
Table 5-2: Top 40 bigrams for waitresses and customers………………………………..
Table 5-3: Binary classification matrix comparing estimated likelihoods to
human ratings………………………………………………………………...
Table 5-4: Binary classification matrix comparing estimated likelihoods to
human ratings………………………………………………………………...
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Chapter 1
Introduction
Conversation is a collaboration. The sequence of utterances, “How are you today?” “Table for
one please,” only makes sense because we understand the social and cultural context. This
verbal exchange conjures images of a greeting between a customer and an employee of a
restaurant. Taken out of context, these words appear to be a non sequitur, yet we understand
them in the context of the “script” that we’ve all learned through myriad trips to a restaurant.
These scripts serve as the common ground for the collaborative activity of dialogue, which
allows the two actors in the script to move jointly towards common sub-goals, which may
ultimately contribute to different role-dependent goals. In this case, the goal for the customer is
to have a nice a meal at the restaurant, while the waitresses’ goal is to sell a meal at good price.
We look forward to a future where robots collaborate with humans in the home and workplace,
and virtual agents collaborate with humans in games and training simulations. A representation
of common ground for everyday scenarios is essential for these agents if they are to be effective
collaborators and communicators. Effective collaborators can infer a partner’s goals and predict
future actions. Effective communicators can infer the meaning of utterances based on semantic
context. This thesis introduces a computational cognitive model of common ground called a
Plan Network. A Plan Network is a statistical model that provides representations of social
roles, object affordances, and expected patterns of behavior and language. I describe a
methodology for unsupervised learning of a Plan Network using a multiplayer video game,
visualization of this network, and evaluation of the learned model with respect to human
judgment of typical behavior. Specifically, I describe learning the Restaurant Plan Network
from data collected from over 5,000 players of an online game called The Restaurant Game.
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Figure 1-1: The Restaurant Game was developed with the Torque game engine, and content from The Sims 2.
1.1 Motivation
Schank and Abelson (1977) were the first to recognize that providing machines with
representations of common ground is essential for their understanding of everyday scenarios, but
in 1977 they were ahead of their time. With the technological limits of the 1970’s, they could
only provide common ground in the form of hand crafted scripts. It would be a simple enough
task to handcraft a restaurant script where a customer sits down and says “Bring me a steak”, a
waitress brings a steak, and the customer pays the bill. In reality, however, there is an infinite
variety of action and dialogue sequences that take place in this scenario. There are limits to the
range of behavior that human scripters can possibly anticipate. Hand crafted scripts are brittle in
the face of unanticipated behavior, and are unlikely to cover appropriate responses for the wide
range of behaviors exhibited when players are given minimal instructions to play roles in an
open ended environment. Furthermore, scripted characters have no means of detecting unusual
behavior. Today we can do much better.
Today, there are millions of people playing video games together online. Von Ahn recognized
this potential, which he leveraged with The ESP Game (2004) to collect a large image labeling
corpus. With The Restaurant Game, I have harnessed the power of the internet to quickly
capture an identical scenario played by thousands of pairs of people. In four months, The
Restaurant Game has generated a corpus of over 5,000 examples of dramatic role-play in a
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restaurant environment. Each gameplay session takes an average of 10 minutes, and consists of
about 85 physical actions and 40 lines of dialogue produced through the interaction of two
human players. It’s safe to assume that any English speaking on-line game player is familiar
with the social conventions followed in a restaurant, and subconsciously maintains a script for
expected behavior in such an establishment. Over many games, we see that a Plan Network
emerges, consisting of a common collection of goals, and a variety of utterances contributing to
jointly satisfying these goals.
1.2 Learning Plan Networks
A plan refers to a sequence of actions taken to satisfy some goal. In the classical description of
logical planning found in Artificial Intelligence text books like Russell and Norvig (1995), a goal
is defined as some state of the world that an agent is trying to reach, and an action is an operation
that changes the state of the world. The restaurant scenario consists of two agents (a customer
and waitress) reaching a series of goal states. Social conventions require collaboration between
the agents to reach many of these states. The first goal state is satisfied when a customer is
sitting in a chair holding a menu. Social conventions require the waitress to show the customer
to a table, and hand him a menu. Following goal states include the customer having eaten one or
more dishes, the waitress collecting a paid bill, and the customer exiting the restaurant. A
collaborative plan is formulated by the agents to accomplish each goal, and the restaurant
scenario as a whole is accomplished through an ordered sequence of plans. The ordering of
plans is not governed by physical dependencies -- a customer could choose to pay his bill before
eating anything, however social conventions say that the waitress brings the bill after the
customer has consumed his meal.
Each play session of The Restaurant Game produces a log file containing a trace of plan
execution for the sequence of plans carried out by one pair of players. The trace provides a
perfect record of actions taken by each player, and resulting changes to the state of the world.
Since the initial state of the restaurant is the same for every game, and all state changes during
gameplay are recorded, it is possible to learn the preconditions and effects of actions from the
execution traces. Merging the execution traces from multiple gameplay sessions produces a
graph-like network of actions, linked to one another by preconditions and effects. An agent who
wishes to play one of the roles in the restaurant scenario can follow one of the paths in this
network to guide his or her actions.
From a traditional A.I. planning perspective, what is learned from the execution traces is really
an action network, because there is no explicit representation of the goals that the actors are
trying to achieve. In this work, I am making the assumption that all behavior is intentional, and
all state changes lead to a world state that is a goal or a sub-goal for the actors. In an
unsupervised system that learns solely by observing physical behavior it is not possible to
capture an actor’s intentions, but by computing the frequencies of action sequences, the system
can filter out many of the world states that are unlikely to be goals of the actors. Given this
assumption of intentional behavior, what was once an execution trace for a human player now
becomes a plan to be followed by an agent. From an agent’s perspective, the network of actions
is a network of potential plans, hence the name Plan Network. Merging the execution traces into
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a graph of shared nodes provides agents with better coverage of the possibility space than could
be achieved by selecting any single execution trace to follow.
Each node of the learned Plan Network is a physical action taken by either the waitress or the
customer. By computing the statistics of actions taken by actors, the system learns social roles.
For example, it learns that waitresses carry food from the kitchen to tables, and customers eat
food while sitting at tables. Similarly, computing statistic of actions taken on objects allows
learning role-specific affordances of objects. Chairs and stools are for customers to sit on. Steak
and salad are carried by waitresses to tables, where they are eaten by customers. Automatically
clustering objects by affordances into concepts like food and dirty dishes allows merging of
action nodes that refer to these objects, producing a more generalized Plan Network.
Between each pair of action nodes in the Plan Network, there is a directed edge. An edge
indicates that the system has observed one action following another. There is a probability
associated with each edge, and a probability distribution at each node over all outgoing edges.
Moving along an edge implies the passage of some amount of time, during which it is possible
that the actors engaged in dialogue. The Plan Network stores all dialogue sequences observed
between two actions with the associated edge. In effect, these dialogue sequences are clustered
and grounded by the semantic context provided by the surrounding physical actions. The Plan
Network provides a model of context that can encode different meanings of the same behavior
given the physical positions of actors, the role of the agent, and the interaction history. For
example, a waitress picking up a half-eaten steak from the table after the customer has eaten
dessert, stood up, and paid the check is different from a waitress picking up the steak while the
customer is still sitting and has just taken his first bite of steak. In the first case, the waitress is
cleaning up a dirty table after the customer finished his meal. In the second, the waitress is
probably bringing the steak back to kitchen because it was not cooked to the customer’s liking.
1.3 Evaluating Plan Networks
Learning a Plan Network from data is only a worthwhile pursuit if the learned model is of high
quality. Quality is measured by how well the model’s assessment of likelihood for observed
behavior and language correlates with human judgment of typicality, and how well the Plan
Network covers the possibility space of what people will do in the restaurant environment.
Graph visualizations of physical behavior and an interactive conversation browser alleviate the
difficult problem of qualitatively evaluating such a large corpus. Quality can be measured
quantitatively by borrowing modeling techniques from the field of Natural Language Processing.
Chapter 5 demonstrates that the learned Plan Network does correlate well with human judgment
of typical restaurant behavior by applying n-gram modeling techniques to estimate the likelihood
of sequences of actions and words, and comparing these estimates to human ratings of 300 log
files.
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1.4 Outline of the Thesis
Chapter 2 reviews related work in the fields of cognitive science, chat bots, video games, and
natural language processing. Chapter 3 describes data collection with The Restaurant Game,
including details on the design and development of the game. Chapter 4 describes how Plan
Networks are built and visualized from the raw game log data. Chapter 5 describes the
evaluation methodology, and presents the results of the evaluation. Chapter 6 concludes with
contributions of the thesis and a plan for future work.
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Chapter 2
Related Work
This thesis draws from a range of influences, beginning with early work in cognitive psychology
and chatbots, continuing through the latest innovations in computer games, language
understanding, and learning plans.
2.1 Cognitive Psychology
Schank and Abelson (1977) introduced the idea that the human ability to understand stories and
infer the missing details relies on the scripts that we’ve learned from childhood. Scripts consist
of roles for people and objects in the story, entry conditions, and a sequence of scenes that
capture a chain of dependent events at an abstract level. When we read that “John went to a
restaurant. He ordered chicken. He left a large tip”, we can infer that he also sat down, looked at
a menu, ate his chicken, and was pleased with it. These ideas were implemented in computer
programs that relied on hand written scripts, and thus could only make inferences for questions
predetermined by their programmers. Current computer game technology allows us to simulate a
restaurant at a high level-of-detail, and exploit the gameplay experiences of thousands of players
to capture a wider coverage of knowledge than could be hand crafted by a team of researchers
decades ago.
Clark (1996) has theorized that language is a form of joint action. The actors in a dialogue can
infer each other’s meaning because they are taking part in a joint project with a common goal,
and shared common ground. Shank’s scripts are one example of common ground; knowledge of
the situation that we can assume everyone shares. Within the script for dining in a restaurant,
there are a number of joint projects, such as seating customers, taking orders for food, and
paying the check. These joint projects are collaborative activities that require participation and
communication from both the customer(s) and the waiter.
Clark views conversations as sequences of paired actions. Garrod and Pickering (2004) discuss a
similar mechanism, and they argue that the existence of these paired actions reflects the fact that
humans are designed for dialogue, rather than monologue. The conversation browser described
in Chapter 4 demonstrates how the Plan Network captures and clusters paired actions, including
speech acts in response to physical actions, and speech acts in response to other speech acts.
Bruner (1977) describes how two years olds acquire language through participating in social
interaction language games. Through repeated interaction, an infant learns the social situation,
which acts as scaffolding for language. Initially the language is redundant with the physical
interaction. Over time, the child learns to predict actions, and is able to swap roles with the
caregiver, and eventually substitute the redundant language for physical action. In a similar
spirit, The Restaurant Game provides many examples of repeated interactions in the same social
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scenario. These interactions provide scaffolding for the language that players use between
actions, and allow the Plan Network to learn the semantic context of utterances. Chapter 4
demonstrates how this scaffolding can be leveraged for interactive browsing of learned
conversations.
2.2 Chatbots and Video Games
The first chatbot was a virtual therapist named ELIZA (1966). While ELIZA’s memoryless,
purely associative, bag-of-words conversational system was able to fool people in the 1960s,
today’s audience is more sophisticated and demands more than templated, formulaic, canned
responses.
Recently, an experimental game Façade (2005) has revisited the natural language problem, and
has cast it within the context of a strong narrative. The human in Façade plays the role of a
guest invited to a couple’s apartment for cocktails, and witnesses the breakup of their marriage.
The player can interact with the environment and freely type conversational text. It took two
researchers five years to handcraft scripts for Façade, and the game takes about 15 minutes to
complete. In Schank’s work, hand crafted scripts lead to brittleness in handling unanticipated
events. Hand crafted scripts in Façade force the narrative down one of the preconceived paths.
In either case, the experience of a user interacting with the system will be limited by the range of
user behaviors that the human script authors anticipated. This thesis takes first step towards
automatically authoring a similar experience in dramatically less time by capturing human input
and responses. Capturing what players actually choose to do in the environment, rather than
what designers expect them to do, will provide responses to a wider range of player behaviors
than a small number of script authors could anticipate.
The Sims (2000) designer Will Wright has coined the term massively single player game to
describe his upcoming creation Spore (2008). As opposed to a massively multiplayer game, like
World of Warcraft (2004), where thousands of humans play on-line together, in a massively
single player game, each person plays the game alone, but benefits from the participation of
potentially millions of other players on the internet. As players of Spore create unique creatures
and environments, everything they create will be automatically uploaded to centralized servers,
and shared with all other players. In this way, the game is constantly evolving on the player’s
desktop.
Wright’s approach bears parallels to the Web2.0 movement, where users of MySpace.com and
Flickr.com are empowered with tools to create shared content that benefit all other users. Luis
von Ahn’s ESPGame (2004) is another multiplayer game that entices people to do Human
Computation, where people do work that is difficult for machines but easy for humans, such as
labeling images. Similarly, here we are trying to harness the power of the internet to attack the
difficult problem of natural language. Like Spore, a game powered by a learned Plan Network
would evolve and improve as people provided new conversational content by playing the game.
Orkin (2004, 2005, 2006) introduced the application of logical planning systems to video games
to give agents adaptive reasoning capabilities in combat scenarios. In this planning system, plan
operators were hand coded by an engineer, and communication and collaboration of agents was
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implemented in an ad hoc layer above the planning system. This approach to planning for
characters with a handful of goal, actions, and utterances worked well for a first person shooter
genred game, but would be impractical for modeling the behavior and dialogue of multiple actors
in a social situation. Chapter 4 provides data documenting the enormous potential plan space of
the restaurant scenario that dwarfs any plan space found in a current generation shooter.
Many of the points raised by Barbara Grosz (1994) about collaborative systems are still yet to be
addressed in computer games. Grosz describes how agents in collaborative systems need to plan
jointly with a commitment to mutually supporting one another. The collaborative plan is not the
sum of the individual plans. Agents need to assume an attitude of intending-that in respect to a
coloration partner, in addition to the attitude of intending-to do things itself. A customer intendsto eat food, which is accomplished by intending-that the waitress will bring the meal that he
ordered. Neither actor can complete the goals that make up the restaurant scenario on their own;
at least not without breaking accepted social conventions. The role-specific visualizations in
Chapter 4 illustrate how Plan Networks can equip agents with models of other minds. These
models provide perspectives into the interleaved collaborative plan, guiding decisions of action
selection that will lead to mutual support.
2.3 Language Understanding
Gorin, Riccardi & Wright (1997) demonstrated unsupervised language acquisition for the AT&T
How May I Help You call routing system. Their system generated a database of utterances from
10,000 spoken transactions, and computed mutual information to extract salient phrases with
high likelihoods of indicating a particular type of call; for example, distinguishing between
customers with billing questions, and those wanting directory assistance.
Gorniak and Roy (2005) used Neverwinter Nights (2002) to demonstrate the effect of plan
recognition on understanding language in the context of a physical situation. This study
collected language from pairs of humans solving a puzzle involving movement between rooms,
and unlocking doors. Gorniak and Roy demonstrated that a probabilistic plan grammar can be
used to recognize affordances and predict the next human action. Their system could
successfully handle underspecified, ambiguous language such as “Do it now!”
The Open Mind Common Sense (2003) project uses the internet to collect common sense facts
expressed in plain English from a large number of people. GloBuddy (2004) is one OMCS
application, where the user describes a situation in text, and the system offers potentially relevant
phrases in a foreign language. This thesis combines these ideas, learning a Plan Network that
combines action and language in a statistical model of common ground that associates relevant
utterances with semantic context.
2.4 Learning Plans
There has been previous work on learning plans. Nilsson (1998) describes how Minton’s
Explanation-Based Generalization (EBG) (1989) can be used to learn new STRIPS rules based
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on previously existing STRIPS rules. EBG is a technique for generating new rules by composing
existing rules with known facts about the world, and generalizing these rules for maximum
utility. A number of researchers including Shen (1994), Gil (1992), Wang (1995), and Benson
(1997) have explored learning action models for agents that have a large number of available
actions, but do not know their preconditions and effects a priori. The process of building a Plan
Network involves learning the action lexicon itself in an unsupervised, bottom-up manner
directly from data. Zettlemoyer, Pasula, and Kaelbling (2005) learn STRIPS rules from training
data in a stochastic 3D simulated blocks world with realistic physics. The Restaurant Game is
not a stochastic environment. Effects of actions are deterministic, however the language used in
dialogue between actors affects the probabilities of next actions. Furthermore, what separates
this thesis from previous work on learning plans is that a Plan Network goes beyond learning
planning rules by also learning socially acceptable sequences of plans.
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Chapter 3
Data Collection with The Restaurant Game
The Restaurant Game launched online on February 21, 2007. 3,355 unique players have
completed 5,200 games between February 21 and June 25, 2007. A game is considered complete
if two players joined, both players typed chat text, and at least one player filled out the aftergame survey. This chapter describes The Restaurant Game as a data collection device, and
details the flow and sources of the data.
3.1 What is The Restaurant Game?
The Restaurant Game is a 3D simulation which anonymously pairs players online to play the
roles of a customer and a waitress in a virtual restaurant. Each player is given a vague objective;
the waitress tries to earn money, while the customer has dinner to satisfy his hunger. Players
control characters from a first person perspective, and can manipulate objects in the
environment, and type open-ended chat text to one another.
Figure 3-1: Vague objectives for the waitress.
30
Figure 3-2: Vague objectives for the customer.
The Restaurant Game affords players an unusual amount of freedom, compared to other games.
While many players do what would expected of people in a restaurant, players are free to take
the story in any direction they choose, including stealing the cash register, eating the flowers,
biting other players, and spinning around on the counter while holding the microwave. In spite
of this apparent freedom, this thesis relies on shared assumptions of what one does in a restaurant
to limit freedom in natural ways.
Within approximately 10 minutes, a pair of players play through a typical (or atypical) restaurant
experience, including reading a menu, ordering food and drinks, eating, and paying the bill. All
physical actions and chat text is logged on centralized game servers. The game concludes with a
brief survey that asks players to describe the customer or waitress, and to speculate on the
personality of the real person playing the opposite role.
3.2 Development of The Restaurant Game
I developed The Restaurant Game over the course of about five months. The game is built on
top of the Torque game engine from Garage Games (2005, 2006). Torque was chosen for its
cross platform support, networking capabilities, active online community, and its inexpensive
licensing fee. The game runs on Windows and Mac OSX.
Most of the content (3D models, textures, and audio) in the game was exported from EA’s The
Sims 2 (2004) with the help of a modding tool called SimPE (2004). I used Chumbalum Soft’s
Milkshape3D (1996) to animate Sims models and to export models in Torque’s DTS format, and
the QuArK (Quake Army Knife) (2001) level editor to construct the shell of the restaurant.
31
Multiple dedicated server processes are running on multiple machines. A dedicated server is a
lightweight non-graphical application that hosts two players in a stand-alone instance of the
restaurant. 10 or 20 of these server processes run on each machine. Over the course of the
project a total of between 30 and 80 server processes have been running on between three and six
machines; mostly Windows boxes with one Mac G5. With 80 servers running at the height of
the project, 160 players could be accommodated in 80 simultaneous games. In reality, the traffic
peaked at 16 simultaneous games, but running an array of servers provided redundancy in the
event of server failure, and helped balance the load. Garage Games runs a Master Server that
allows Torque client applications to find Torque servers over the internet.
3.3 Design Considerations for The Restaurant Game
The desire to capture an accurate model of conversational behavior in the restaurant scenario
dictated a number of design requirements. The game needed to be accessible to a wide audience,
encourage natural conversation, provide freedom for dramatic role-playing, and hold players
interest long enough to complete one or more games. Following are the considerations taken
into account to meet each of these requirements.
3.3.1 Accessibility to a Wide Audience
The game needed to be simple to play for people with any level of gaming experience in order to
maximize the number of potential players, and to ensure capturing data from a large crosssection of the population. All interaction is mouse-driven with a point-and-click interface, and a
pop-up interaction menu. The left mouse button selects objects to interact with, and the right
mouse button moves the character. (Players with one-button mice can use the arrow keys to
move). Moving the mouse controls the viewpoint of the player. Prior to joining a multiplayer
game, a 5-10 minute tutorial walks the player through instructions on how to interact with the
world, without assuming any particular role (waitress or customer). Players learn to pick up, sit
on, eat, inspect, and touch things, and to create bills, chat, and order food.
32
Figure 3-3: Interface for interacting with objects with icons inspired by The Elephant’s Memory visual
language (1994).
3.3.2 Player Retention
Once a player with capable hardware has been enticed to install the game and complete the
tutorial, there are a number of measures in place to encourage this player to complete a game
online one or more times. Players are informed that will be credited as Game Designers on a
future game generated with the data collected from completed online games. The open-ended
nature of the game keeps the experience fresh. The post game survey also acts as a motivator to
play multiple times. The survey asks players to speculate on the personality of the real person
playing the other role, rating their intelligence, sense of humor, honesty, consideration,
eloquence, and patience, and to guess their occupation, geographic location, gender, age, and
what they eat for breakfast. Results of the survey are presented to the other player, accumulated
and averaged over all games played. Players are motivated to see their cumulative survey evolve
over several games; people love to hear about themselves. Finally, the amount of virtual money
in each player’s account persists. Players start with $50, and usually spend this money as a
customer and earn money as a waitress. The intent is to encourage playing multiple times in
different roles, under different financial constraints.
33
Figure 3-4: Post-game survey.
3.3.3 Natural Conversation
It is important to ensure there are no interface obstacles to players conversing, in alignment with
the goal of capturing conversation in context. Any keyboard input is treated as chat text,
allowing players to freely type open-ended text to each other at any time, without having to press
any buttons first. The first-person perspective was a deliberate design decision to encourage
natural situated and embodied dialogue, with players facing each other. Players can only hear
one another when the speaker is facing the listener. Otherwise, the listener receives a message
that the other player is speaking with his or her back turned, or is speaking to the staff. The
scripted chef and bartender will only acknowledge orders for food and drinks if the speaker is
facing the staff member, and within a reasonable proximity.
34
3.3.4 Freedom for Dramatic Role-Playing
The premise of this thesis is that modeling behavior by observing thousands of players will
capture behavior that human scripters would have never anticipated. To demonstrate that this is
the case, players are given the freedom to play through the scenario any way they choose. Video
games typically offer players context-sensitive interactions with objects, and many objects in the
world are purely decorative and non-interactive. The Restaurant Game does not presuppose
desired interactions, and instead allows the player to interact in a consistent way with every
object in the game world. Players get satisfying responses from every object when they try to
pickup, inspect, touch, eat, or sit on them. The effect of these actions on the world varies
depending on the type of action. For example, food visually diminishes when eaten, while eating
a chair produces a chomping sound with no visual change. Touching (or operating) the cash
register produces a bill, touching the microwave results in beeping sounds, touching a plate of
food rotates it on the table. The other player receives narration to clarify events that do not
visually change the world (e.g. “Waitress bit the chair”).
The open-ended design of The Restaurant Game subscribes to Will Wright’s definition of “good
game aesthetics.” As Wright describes in Feldman’s Gamespot interview (2003) about
designing The Sims 2:
Players obviously enjoy being subversive to some degree. And so we want to provide
that and let them know that we’re on their side in that way […]. It’s just another form of a
player taking control. I think for most people, their kind of general aesthetic with games is
that the more I control this experience, the better the game is […] in terms of [finding that]
I can choose to go off into an interesting path, and the game will support that path. This
animal we’re calling subversion is really just empowering the players to not hit walls as
often […]. At a fundamental level it’s kind of convergent with what I would call “good
game aesthetics.”
Players have, in fact, surprised me with unanticipated behavior, such as making a margarita by
carrying the fruit bowl from the kitchen to the bar and using the blender. More than a few
players have chosen to dramatize what happens when the customer steals the cash register. In
The Restaurant Game, the social scenario is known a priori, but this will not always be the case.
As user-created content in virtual worlds like Second Life (2003) become more prevalent, agents
in the future will require the ability to learn expected social behavior in novel situations.
Other than their outfits, the game does not enforce any assumptions about social roles. The
waitress does wear a uniform, but either player can eat food, request menu items from the staff,
and operate the cash register to create or deposit bills.
The open-ended nature of the game provides a challenge in knowing when to consider a game
complete, as opposed to prematurely aborted, intentionally or unintentionally. For instance,
relying on detecting that the customer has eaten some food and paid a bill presupposes
assumptions about this scenario. Initiation of the post game survey provides a good indicator
that at least one player feels that the scenario has concluded. The survey also helps screen out
35
data from troublemakers who are not interested in contributing to research, because these abusive
players often choose not to fill out the survey.
3.4 Where Does Data Come From?
This project began with many concerns. Will anyone actually play this game? Will people be
willing to install a unique stand-alone application? Will people manage to find partners online at
the same time? I originally aimed to collect 1,000 completed games. To my surprise, this goal
was achieved in under two weeks, and the project continued on to collect over 5,000 games. The
servers are still up, and over 100 games continue to trickle in every week. Who are these people,
and how did they find out about The Restaurant Game?
3.4.1 Rallying the Masses
On February 21, I started the servers and posted the project web page,
http://theRestaurantGame.net, consisting of a brief description of the project and downloadable
installers for Windows and OSX. The page encourages people to participate with the following
enticement:
Contribute to the first collaboratively authored computer game and earn Game Designer
credit!
The Restaurant Game is a research project at the MIT Media Lab that will algorithmically
combine the gameplay experiences of thousands of players to create a new game. In a
few months, we will apply machine learning algorithms to data collected through the
multiplayer Restaurant Game, and produce a new single-player game that we will enter
into the 2008 Independent Games Festival. Everyone who plays The Restaurant Game
will be credited as a Game Designer.
I announced the project quietly on Game/AI, a blog shared by game industry AI developers with
a small readership (bloglines.com reports 67 subscribers). Within the week, several other
gaming-related blogs posted about the project, including Grand Text Auto and TerraNova. This
provided a good crop of players for beta testing and tweaking/bug fixing before announcing the
project to wider audience a week later.
Once all major issues were resolved, word went out via email to six game industry websites, five
Mac websites, and acquaintances at 24 software companies and 12 universities with game
development programs. In addition, I listed the game on two shareware download sites, and two
friends distributed fliers at conferences (the Game Developers Conference, and Human Robot
Interaction). The flier at HRI caught the eye of a reporter from New Scientist magazine who
wrote a short article in March (2007). Digg.com and Del.icio.us icons on the project webpage
also helped spread the word through social bookmarking. More recently, a PCWorld.com review
of the game was highlighted on MSN.com, drawing another wave of players.
36
Table 3-1 details various gameplay statistics. A total of 5,602 players logged into the game, but
many failed to find a partner online at the same time, and never tried again. The first player to
join a server is always the waitress, who must wait for a customer to show up.
Total Players Logged In
Players Who Completed Games
5,602
3,355
Game Duration
Waitress Wait Time
Games Played Per Person
Mean
11.18 min
2.41 min
3
Games Over 20 min
Waitress Wait Times Over 20 min
427
31
Table 3-1: Gameplay statistics from 5,200 games.
37
Median
9.82 min
1.12 min
2
Max
94.2 min
78.31 min
77
Below is a graph of the number of games completed per week. The peaks correspond to weeks
when The Restaurant Game was featured on popular video game news sites, or on MSN. The
March 24th New Scientist article was one of the biggest draws of players, but does not correspond
to a peak on this graph; rather it lead to a longer lasting steady stream of new players.
Games Completed per Week
1200
# of Games Completed
1000
800
600
400
200
6/
20
/0
7
6/
13
/0
7
6/
6/
07
5/
30
/0
7
5/
23
/0
7
5/
16
/0
7
5/
9/
07
5/
2/
07
4/
25
/0
7
4/
18
/0
7
4/
11
/0
7
4/
4/
07
3/
28
/0
7
3/
21
/0
7
3/
14
/0
7
3/
7/
07
2/
21
/0
7
2/
28
/0
7
0
Figure 3-5: Games completed per week.
The next graph illustrates the number of page hits to the project webpage per week, as reported
by Google Analytics. Between February 21 and June 25, Google reports that the page has been
hit a total of 49,343 times by 42,358 unique visitors.
Web Page Hits per Week
10000
9000
8000
Web Page Hits
7000
6000
5000
4000
3000
2000
1000
6/
13
/2
00
7
6/
20
/2
00
7
6/
6/
20
07
5/
16
/2
00
7
5/
23
/2
00
7
5/
30
/2
00
7
5/
9/
20
07
5/
2/
20
07
4/
11
/2
00
7
4/
18
/2
00
7
4/
25
/2
00
7
4/
4/
20
07
3/
14
/2
00
7
3/
21
/2
00
7
3/
28
/2
00
7
3/
7/
20
07
2/
21
/2
00
7
2/
28
/2
00
7
0
Figure 3-6: Project web page hits per week.
In addition, 7,501 people have downloaded the game from shareware download websites. So, in
total the project attracted an audience of 42,358 + 7,501 = 49,859 people (who either read about
and/or played the game). Dividing the 5,602 unique players who logged in by the audience of
38
49,859 people yields that about 11% of the audience actually played the game. The 3,355
players who completed games account for about 60% of those who logged in, and about 7% of
the total audience.
3.4.2 Player Demographics
Other than confirming that they are over 18, the game does not request any personal information
from players. A bit of information about players can be gleaned through volunteered
information, Google Analytics, and automatic platform detection.
The game’s login process allows players to voluntarily self-report where they heard about the
game. 87% of the players who completed games reported, and the results of the survey are
below. “Game Industry Sites” encompasses 22 different game industry news and blog sites.
“Word of Mouth” covers everyone who reported hearing about the game through friends, family,
instructors, or colleagues. “Other” combines counts from 353 unique responses that did not fit
any other category.
Where Players Heard About The Restaurant Game
487
Game Industry Sites
Word of Mouth
1006
Mac Sites
Shareware Download Sites
389
Search Engines
New Scientist
Social Bookmarking Sites
MSN
227
75
Ars Technica
Other
96
138
176
163
165
Figure 3-7: Where players heard about The Restaurant Game.
The client application detects the hardware and OS it is running on, and reports this to the server.
The pie chart below details the number of games completed on each platform. Note that each
game is played on two clients at once, so the total is 10,400 for the 5,200 completed games.
39
Games Played per Platform
1027
1245
MSWindows
Mac(PPC)
Mac(Intel)
8128
Figure 3-8: Games played per platform.
Google Analytics provides the geographic breakdown of where viewers of the project web page
are located in the world. A significant number of players come from non-English speaking
countries. The account creation process requires players to report their level of English
proficiency, so that non-native speakers can be screened out if desired. The experiments in this
thesis do not screen out anyone, but the results in Chapter 5 demonstrate that the statistical
models are able to identify gameplay sessions containing foreign languages or broken English.
40
Figure 3-9: Geographic distribution of players as reported by Google Analytics.
3.5 Lessons Learned About Game-Based Data Collection
There are many factors to consider when designing an online game for collecting data in the wild
of the internet, and it is unlikely that the first iteration of the system will be perfect. This section
describes lessons learned from the experience of launching The Restaurant Game; the first
iteration of a data collection platform.
41
The design considerations detailed in section 3.3 contributed to the success of the data collection
effort. The simple installation and user interface were amenable to a wide audience of thousands
of players. Players seemed to enjoy open ended dramatic role playing and learning about others’
impressions of themselves through post-game survey. It is difficult to tell how much impact the
enticement of Game Designer credentials on a future game had on players, but this gimmick
certainly helped attract attention from blogs and news web sites. However, the first lesson
learned was the danger of promising the public a deliverable based on novel research by a
specific date. The project web page promises Game Designer credit on a new game that will be
entered into the 2008 Independent Games Festival (IGF 2008). The goal of automating
characters with the learned Plan Network remains, but this will not be achieved in time for the
IGF 2008 deadline.
3.5.1 Lessons Learned About Publicity
Regardless of the outcome, the promise of entry into the IGF proved newsworthy. New versions
of The Restaurant Game and support for additional platforms also provided newsworthy
information to report, and re-generate excitement and awareness of the project. Updated
versions were necessary to fix minor bugs and expand supported platforms, but were equally
useful in generating press. Blogs and web sites only re-post about a project if there is something
new to report. While the great majority of players were Windows-based, supporting multiple
platforms was worthwhile for broadening press coverage. The Mac community greets crossplatform supported software with enthusiasm, and there are a variety of Mac web sites that reach
a heterogeneous cross-section of the public.
The Mac web sites reached audiences for The Restaurant Game that I did not realize existed, like
the food service and hospitality industries. The project attracted interest from producers of a
BBC production about restaurant management, a blog devoted to hotel technology, and the
National Restaurant Association. How the word of a project like this spreads is unpredictable. I
learned two lessons from missed publicity opportunities.
Word of the project reached a journalist writing an article about AI in games for The Boston
Globe. The article ran in June, and mentioned The Restaurant Game, but neglected to print the
URL. It is best to explicitly ask that press coverage includes information necessary for readers
who wish to participate in the research.
The second missed opportunity was related to the social bookmarking sites Digg.com and
Del.icio.us. Unfortunately, the biggest spike in activity came early in the project when two video
game news sites posted about it, before I had added the Digg and Del.icio.us badges to the
project web page. Visitors who click these badges increment the count of the number of people
bookmarking the page. Prominent link placement on the main Digg and Del.icio.us pages
depends on the rate of increase of people clicking these badges. Had I added the badges from the
start of the project, these social bookmarking sites may have helped the project reach a much
wider audience.
42
3.5.2 Lessons Learned About Third Party Technology
When activity does spike, it is useful to know the source of the spike. I relied on Google
Analytics to find referring sources of web traffic. This information is not only useful for
planning where to advertise in the future, but also for observing what information players are
sharing with one another, some of which may be malicious. I learned of a number of issues by
reading forum posts to video game news sites that reported on the project.
Players were teaching each other to exploit a bug that allowed waitresses and customers to
literally fly through the roof by picking up the object that the player was sitting on. Players
familiar with the Torque game engine were able to open the console and type commands such as
suicide, to kill and re-spawn a player, wreaking havoc on the scenario. Most alarming, some of
these players were aware that the same Torque executable is used for the client and server, and
were attempting to run their own servers. This would have been a disaster, as I would never
have access to the data collected by these rogue servers. Fortunately, these deviants never
figured out all of the command-line parameters required to start a server, and gave up. The
lesson learned is that extra precautions need to be taken when using third party technologies.
The Restaurant Game should have been released with the command console disabled, and server
functionality removed.
3.5.3 Old Habits Die Hard
The video game press attracts many capable players with an interest in novel games, however
these players bring habits learned from previous gaming experiences. The data reveals a number
of prevalent habits such as trying to move the character with the AWSD keys (the standard
control scheme for first person shooters), and beginning each line of chat text with the word text,
as some online games require. These patterns are easy to detect and screen out of the dialogue
data. Other habits highlight missing features that multiplayer gamers expect, and may be
desirable in future data collection games. Players often try to augment the limited set of
animations by typing World of Warcraft-style emotes, such as /wave, /weep, /kiss, /burp, or
/applaud. These emotes do not currently result in any animations, but adding some of these
animations might enhance the interaction, and allow the system to learn gestures and body
language as well as spoken language. Additional interface problems were related to foreign
keyboards, and one-button mice.
43
Chapter 4
Building and Visualizing Plan Networks
The Restaurant Game has captured a large quantity of data, but in its raw form, it’s not of much
use to a human and much less to an agent. Following is an excerpt from a log file exported from
one gameplay session.
44
[COGLOG] 6061977 EVENT CUSTOMER(Player) ENTERED doorTrigger(RegionTrigger)
[COGLOG] 6062055 STATECHANGE CUSTOMER(Player) ATPOS=”(321.146 358.792 240.541)”
[COGLOG] 6063446 EVENT WAITRESS(Player) FACING CUSTOMER(Player)
[COGLOG] 6063446 STATECHANGE WAITRESS(Player) FORWARDDIR=”(0.989292 -0.145949 0)”
[COGLOG] 6063446 STATECHANGE WAITRESS(Player) ATPOS=”(314.443 359.182 240.54)”
[COGLOG] 6063446 SPEECHACT WAITRESS(Player) “welcome”
[COGLOG] 6066368 EVENT CUSTOMER(Player) FACING WAITRESS(Player)
[COGLOG] 6066368 STATECHANGE CUSTOMER(Player) FORWARDDIR=”(-0.990476 0.137684 0)”
[COGLOG] 6066368 SPEECHACT CUSTOMER(Player) “hi”
[COGLOG] 6069039 SPEECHACT WAITRESS(Player) “please have a seat”
[COGLOG] 6074211 EVENT CUSTOMER(Player) FACING WAITRESS(Player)
[COGLOG] 6074211 STATECHANGE CUSTOMER(Player) FORWARDDIR=”(-0.894119 0.440718 -0.0794913)”
[COGLOG] 6074211 SPEECHACT CUSTOMER(Player) “where?”
[COGLOG] 6077493 EVENT CUSTOMER(Player) EXITED doorTrigger(RegionTrigger)
[COGLOG] 6083586 SPEECHACT WAITRESS(Player) “customers choice”
[COGLOG] 6102211 SPEECHACT WAITRESS(Player) “you choose where you want to sit”
[COGLOG] 6108946 EVENT CUSTOMER(Player) ENTERED podiumTrigger(RegionTrigger)
...
[COGLOG] 6132664 STATECHANGE CUSTOMER(Player) FORWARDDIR=”(0.345101 0.930914 0.119601)”
[COGLOG] 6132664 SPEECHACT CUSTOMER(Player) “ummmm let’s see”
[COGLOG] 6135352 EVENT CUSTOMER(Player) ENTERED table3Trigger(RegionTrigger)
...
[COGLOG] 6139930 ACTION CUSTOMER(Player) SITON chair6(DBChair)
[COGLOG] 6139930 STATECHANGE CUSTOMER(Player) SITTINGON=”chair6(DBChair)”
[COGLOG] 6142821 STATECHANGE WAITRESS(Player) DEST=”(309.925 360.189 240.632)”
[COGLOG] 6142821 ACTION WAITRESS(Player) GOTO table3(DBTable)
[COGLOG] 6142993 EVENT WAITRESS(Player) ENTERED table3Trigger(RegionTrigger)
[COGLOG] 6143039 ACTION WAITRESS(Player) PUTDOWN dynamic2753(DBMenu)
[COGLOG] 6143039 STATECHANGE dynamic2753(DBMenu) ATTACHEDTO=”NULL”
[COGLOG] 6143039 STATECHANGE dynamic2753(DBMenu) ON=”table3(DBTable)”
[COGLOG] 6143039 STATECHANGE dynamic2753(DBMenu) ATPOS=”(311.177 360.455 242.635)”
[COGLOG] 6145123 ACTION CUSTOMER(Player) PICKUP dynamic2753(DBMenu)
[COGLOG] 6145123 STATECHANGE dynamic2753(DBMenu) ON=”NULL”
[COGLOG] 6145123 STATECHANGE dynamic2753(DBMenu) ATTACHEDTO=”CUSTOMER(Player)”
[COGLOG] 6147118 ACTION CUSTOMER(Player) LOOKAT dynamic2753(DBMenu)
[COGLOG] 6147571 STATECHANGE WAITRESS(Player) FORWARDDIR=”(-0.89335 0.291024 -0.342391)”
[COGLOG] 6147571 SPEECHACT WAITRESS(Player) “here’s a menu. Take your time”
Figure 4-1: A raw log file from a gameplay session.
45
These raw log files are difficult to interpret. It is straight forward to write a program that distills
the logs into something that is more human-readable. Figure 4-2 shows the log file for the same
game distilled into a script, which reads more like a theatrical script to a film or a play.
6044508
6044993
6058118
6063446
6063446
6066368
6069039
6074211
6083586
6102211
6108727
WAITRESS WALKS TO podium
WAITRESS PICKSUP Menu
CUSTOMER WALKS TO door
[CONVERSATION BETWEEN WAITRESS AND CUSTOMER]
WAITRESS: “welcome”
CUSTOMER: “hi”
WAITRESS: “please have a seat”
CUSTOMER: “where?”
WAITRESS: “customers choice”
WAITRESS: “you choose where you want to sit”
CUSTOMER WALKS TO podium
6132664 [CONVERSATION BETWEEN CUSTOMER AND WAITRESS
6132664 CUSTOMER: “ummmm let’s see”
6135196
6139930
6142821
6143039
6144661
6147118
CUSTOMER WALKS TO table3
CUSTOMER SITSON chair6
WAITRESS WALKS TO table3
WAITRESS PUTSDOWN Menu ON table3
CUSTOMER PICKSUP Menu
CUSTOMER LOOKSAT Menu
6147571 [CONVERSATION BETWEEN WAITRESS AND CUSTOMER]
6147571 WAITRESS: “here’s a menu. Take your time”
6150633 CUSTOMER: “I’ll have vegetable soup and the nectarine tart”
6200711
WAITRESS WALKS TO counter
6216133 [CONVERSATION BETWEEN WAITRESS AND CHEF]
6216133 WAITRESS: “one soup please”
6217133
6221461
6222149
6225368
6225430
6226196
6228086
6242774
6242774
6245743
6301211
6301211
6309711
6315243
6316743
Soup APPEARS ON Counter
WAITRESS PICKSUP Soup
WAITRESS PUTSDOWN Soup ON table3
WAITRESS WALKS BETWEEN counter AND table2
CUSTOMER EATS Soup
WAITRESS PUTSDOWN Menu ON MenuBox
Menu DISAPPEARS
WAITRESS: “anything for drink”
CUSTOMER: “water”
WAITRESS: “coming right up”
WAITRESS WALKS TO bar
6320508 [CONVERSATION BETWEEN WAITRESS AND BARTENDER]
6320508 WAITRESS: “one water”
6321508
6324633
6325649
6328743
Water APPEARS ON Bar
WAITRESS PICKSUP Water
WAITRESS PUTSDOWN Water ON table3
Figure 4-2: A filtered script, generated from a raw log file.
46
A human can read through an entire game in a few minutes. These scripts are useful for spot
checking the data, but are not a practical solution to understanding our corpus as a whole.
Reading 5,000 games would take over ten days, but more importantly, merely reading the scripts
is not equivalent to understanding their commonalities and differences. A Plan Network
provides a generalized representation that incorporates everything observed in all gameplay
sessions. This representation gives humans the gist of the behavior in the restaurant scenario,
and provides a means of predicting future actions and recognizing when things are going offscript. The next section looks at ways to visualize a Plan Network, before delving into the
details of how to build such a model in section 4.2.
4.1 Visualizing Plan Networks
Each gameplay session is composed of interleaved sequences of physical actions and speech acts
performed by multiple characters in various social roles. Pulling apart the data based on these
categories will provide perspectives into the data that are easier for humans to comprehend, and
more powerful for planning and recognition by an agent.
4.1.1 Graphing Physical Actions
Figure 4-3 re-renders the log file seen previously in Figure 4-1 as a directed graph of physical
actions. For the most part, the graph follows a linear path from START to END, with just a few
loops for repeated behavior such as the customer eating multiple bites of an entrée. I created this
graph automatically by exporting actions as nodes, and edges representing observed sequences of
actions. In section 4.2, I will describe the process of creating a lexicon of actions, and clustering
similar actions (e.g. eating salmon and eating pie are clustered as eating food).
47
48
Figure 4-3: Graph visualization of one game.
Figure 4-4 depicts another gameplay session. The behavior is this session is similar to that in
Figure 4-3, with a few minor variations. In the second game, the waitress gives the menu to the
customer, rather than placing it on the table. The customer stands up before paying the bill in the
first game, and pays while sitting at the table in the second.
Figure 4-4: Graph visualization of a second game.
Multiple logs can be combined into the same graph, where the paths for each log share nodes for
actions that occur in both gameplay sessions. Below are the results of incorporating a second
log’s sequence into the original graph from Figure 4-3. Nodes and edges are colored red or blue
depending on whether they came from the first or second gameplay session. Nodes and edges
appearing in both sessions are purple.
49
50
Figure 4-5: Graph visualization merging two games.
51
Figure 4-6: Graph visualization merging 5,000 games.
This process can be repeated for every gameplay session observed to generate a comprehensive graph of the action possibility
space for the restaurant scenario. The portion of the graph in the red box will be expanded in Figure 4-7.
Figure 4-7 zooms in for closer inspection of a portion of the complete graph from Figure 4-6.
The graph includes expected restaurant behavior such as waitresses giving drinks to the
customers, and depositing bills in the cash registers, and atypical behavior like customers sitting
on tables and picking up flower vases. The variety in ordering of actions results in a dense mesh
of edges between nodes.
Figure 4-7: Zoomed-in portion of graph visualization merging 5,000 games.
52
This graph is quite complex, illustrating the huge amount of variation in the way pairs of people
choose to play the game. The graph is so complex, in fact, that it is difficult to assess whether it
is an accurate depiction of what people normally do in a restaurant. Leveraging the Plan
Network’s learned statistical models and understanding of social roles can greatly improve these
visualizations.
The likelihood of any action can be computed by counting the frequency with which it occurs
across all gameplay sessions. Setting a threshold on the likelihood allows filtering out of
unlikely nodes and branches in the graph. Chapter 5 describes estimating likelihoods of entire
gameplay sessions, which is useful for filtering atypical sessions out of the graph entirely.
Interleaving the actions of the two actors produces a lot of noise, due to the fact that much of the
time these two are acting in parallel, and one’s next action does not depend on the other. Below
is a graph of behavior for a single social role, the waitress, with statistically unlikely actions
filtered out.
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54
Figure 4-8: Filtered graph of only waitress behavior from 5,000 games.
This graph of filtered waitress behavior is much simpler than the previous graph, yet still gives
the gist of behavior learned from 5,000 games. Beyond visualization, filtering gives the system a
sense of typical behavior, and equipping an agent with separate models of social roles provides a
mental model of others, useful for inferring goals of a collaboration partner. Zooming in on a
few portions of the graph allows verification that the learned behavior is what one would expect
from a waitress in a restaurant.
55
56
Figure 4-9: Filtered graph of only waitress behavior from 5,000 games, with color-coded portions that will be expanded in the following figures.
57
.
Figure 4-10: Beginning of game for waitress, as learned from 5,000 games.
Prior to the arrival of a customer, waitresses clean off tables and collect paid bills. Once a customer arrives and sits down, the
waitress gives him a menu.
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Figure 4-11: Decision point for waitress, as learned from 5,000 games.
After serving a customer, the waitress might serve him more food, or create a bill at the cash register (by touching the register).
59
Figure 4-12: End of game for waitress, as learned from 5,000 games.
Finally, after creating the bill, the waitress puts the bill on the table for the customer and picks it up for deposit once it’s been paid.
The edge in Figure 4 -12 from touching the register to putting the bill down on the register is actually capturing a common point of
interface confusion. Touching the register creates a new bill, but many players try this first accidentally before realizing that
depositing a bill is accomplished by putting it down on the register.
This section has shown that the learned statistics and social roles benefit visualization for
humans. The evaluation in Chapter 5 demonstrates that these techniques are also beneficial in
building a cognitive model that correlates well with human conceptualization of typical
restaurant behavior.
4.1.2 Browsing Conversations
Graphing models of physical behavior give humans the gist of the behavior captured in our data.
This section explores how to get a similar view of linguistic data. Taking inspiration from
Bruner’s theory, the graphical model of physical behavior can provide the scaffolding that lends
meaning to utterances. Between any two physical action nodes, all conversations uttered
between players can be clustered. Following is a screenshot of the conversation browser
application that runs alongside The Restaurant Game. The browser is networked with the game,
tracking events through messages sent via TCP/IP. Each time either player takes a physical
action, the browser updates a display of the likelihood of the current action, and a sorted list of
the most likely next actions. If the user clicks on any of the potential next actions, the browser
displays a list of all conversations observed in between these two actions in the 5,000 captured
games. Each speech act within a conversation is automatically labeled with a speech act
classifier.1
In the first example, a waitress picks up some filet mignon from the counter. The interface
reports that picking up filet mignon is not highly likely on its own, due to the variety of foods
waitresses may pick up, but when clustered with all other food this action is recognized as highly
likely. There is a long list of potential next actions, but the system has a strong preference for
one action – the waitress putting the food down on a table. Clicking on the most likely action
generates a list of 1,593 unique examples of conversations that the system has observed between
the time a waitress picks up a dish and puts it down on a table.
1
Prior to the online study, I conducted an in-lab pilot study with 50 pairs of people. Data from the pilot study was
used to train an HMM-based speech act classifier, implemented using Joachim’s SVMlight HMM package (2004).
HMMs have been shown to be effective speech act classifiers previously in Jurafsky (2003). Classification
performance on the online corpus has not been formally evaluated.
60
Figure 4-13: Browsing conversations after picking up food, captured from 5,000 games.
Once the waitress puts down the dish, the next action is less certain, and language plays a larger
role in determining which branch of the physical graph is taken. Similar to the role-specific
filtering of physical behavior graphs, the browser allows the user to focus on one social role
when browsing conversations. Focusing on the waitress, there are several highly likely branches.
She might clean up some dirty dishes, pick up a drink from the bar, bring more food, or ring up
the customer at the register. If the user selects touching the cash register (to create a bill) as the
next action, the browser displays 316 unique examples of conversations that precede the waitress
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getting the bill. These conversations typically include directives from the customer referring to a
check or bill.
Figure 4-14: Browsing conversations after putting down food, captured from 5,000 games.
Equally as interesting as the conversations clustered within the likely branches of the physical
action model are those that exist within unlikely branches. The following example bolsters the
claim that capturing behavior from human players produces a more robust model than hand
authored scripts. There is a fruit bowl in the back of the kitchen sitting on the dish washer. This
is a purely decorative prop that would be non-interactive in most games, or at best physically
simulated to fall down when pushed. The following screenshot shows that the system recognizes
that the waitress picking up the fruit bowl is highly unlikely. However, in the event that she does
pick it up, the system predicts that she is most likely going to place it on a table, and displays 37
examples of conversations that occur when she does.
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Figure 4-15: Browsing conversations after picking up fruit bowl, captured from 5,000 games.
In one game the customer compliments the bananas. In others the waitress offers a
complimentary bowl of fruit. The Plan Network captures dialogue that human scripters are
unlikely to include, because the Plan Network captures what people really do, rather than
scripting in anticipation of what they are likely to do.
4.2 Building Plan Networks
I will now describe the process of constructing a Plan Network. The first step is to compile a
lexicon of physical actions and a lexicon of words. Next physical actions are automatically
clustered to simplify the network. Finally, frequencies are computed for atoms in both of the
lexicons, where an atom refers to an individual action or word, and for transitions between
atoms.
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4.2.1 Terminology and Representation
We need to define the terms action, action lexicon, language lexicon, and word before we can
proceed. An action is a manipulation of the world performed by an agent, the structure of which
will be examined shortly. An action lexicon is a catalogue of all unique actions observed in
5,000 games. We define word in the usual way, as an atomic unit of language with which
players construct speech acts. A language lexicon is a catalogue of all unique words we have
observed in 5,000 games, including slang, typos, abbreviations, and multiple tenses.
An action is represented in a logical format similar to a STRIPS operator, as described in Russell
and Norvig (1995). Each action is defined by a set of preconditions and effects, where
preconditions are world state variable criteria that must be met to activate this action, and effects
are assignments to world state variables upon completion. In addition, each action has a social
role requirement, and an interaction object. Actions are typically initiated through the user
interface when a player (of some social role) clicks on an object and selects an action from the
pop-up menu. Actions represent preconditions with a short list of variables localized to
describing only the actor and the interaction object.
Here is an example of an action representing a customer picking up a salad (with one bite taken)
from a table while sitting in a chair:
ACTION: PICKUP
REQUIREMENTS:
ROLE = CUSTOMER
OBJECT = Salad
PRECONDITIONS:
ACTOR::SITTINGON = Chair
OBJECT::ATTACHEDTO = NULL
OBJECT::DELETED = FALSE
OBJECT::ON = Table
OBJECT::SHAPE = Bite1
EFFECTS:
OBJECT::ATTACHEDTO = CUSTOMER
OBJECT::ON = NULL
Actions are learned automatically from data, and preconditions are assigned based on the current
state of the world at the time the action’s execution was observed. The scope of the world state
considered for preconditions is limited to the acting agent and the object being acted upon. This
agent-centric world state representation is similar to that described in Orkin (2004, 2005, 2006).
No assumptions are made about which subset of preconditions are required for each type of
action. In other words, every action includes preconditions for SittingOn, AttachedTo, Deleted,
On, and Shape. The system does not have any prior knowledge of these actions, or restaurant
behavior in general. Learning restaurant behavior should be no different to the system than
learning behavior for hospitals, banks, or grocery stores. This leads to under-generalization,
meaning that the above action is unique from another action where the customer picks up the
salad while sitting on a stool, or standing, or picks up a salad with two bites taken. I will explain
shortly how opportunities can be exploited to cluster actions.
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4.2.2 Building an Action Lexicon
As each gameplay log file is processed, unique actions that we have not previously observed are
added to the action lexicon. The first action observed is index one, the next non-identical action
is index 2 and so on. The action lexicon indices are used to compile gameplay logs down to a
compact form, more convenient for statistic analysis. Below is a graph of how the size of the
action lexicon grows as the system processes 5,000 games.
Size of Action Lexicon Over Time
12000
10000
Number of Actions
8000
6000
4000
2000
0
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
Num be r of Ga m e s
Figure 4-16: Growth of action lexicon over 5,000 games.
After 5,000 games the action lexicon grows to 11,206 unique actions, and has not plateaued in
size. The maximum action lexicon size is 107,468, due to all possible combinations of variables
(e.g. eating salmon while sitting on a chair vs. eating salmon sitting on the microwave vs. eating
salmon sitting on the counter, etc). Details of calculating the maximum action lexicon size can
be found in Appendix B. After 5,000 games the action lexicon has only covered about 10% of
the possibility space (computed by dividing the observed 11,206 by the maximum 107,468).
Fortunately, social conventions lead the majority of combinations to be statistically unlikely,
hence the less than linear growth. Table 4-1 gives some statistics of game length.
Actions per Game
Mean
84.96
Median
77
Table 4-1: Game length statistics, based on 5,000 games.
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Mode
75
Max
919
4.2.3 Clustering Actions
Many of the objects in the world of the restaurant are functionally similar. If these objects can
be clustered, then the actions that refer to those objects can also be clustered, greatly reducing the
size of our action lexicon.
Objects are clustered in a bottom-up fashion following these steps: First, count how many games
include someone interacting with each object. Next, tabulate in how many games each action is
applied to each object. For example, count how many times a customer eats steak while sitting
at the table. Finally, count how many times an object provides context for an action taken on
another object. In the previous example, the table and chair are providing context for the action
of eating steak. Dividing the action and context counts by the total interaction count gives the
probability of an action being taken on an object, or an object providing context for an action.
Actions with probabilities above some chosen threshold are considered to be the affordances of
the object. Objects that have an identical list of affordances are clustered. For instance, chairs
are clustered with stools because customers sit on them, and salmon with steak because
customers eat them and waitresses pick them up from the counter. Keep in mind that actions
themselves are role-dependent, allowing the system to learn role-dependent affordances. Once
the clustering process is complete, clusters are manually inspected and assigned labels for
visualization purposes. Labels include concepts such as food, drinks, chairs, and dirty dishes.
Discriminating between the counter and the bar presents a problem for the method described thus
far. The system will not recognize that we pick up food from the counter and drinks from the
bar. The problem is that there are six types of beverages and nine types of food. The probability
of the bar or counter providing context is roughly equal for each of these items, and when
divided six or nine ways the probability of picking up any single item is low. To remedy this,
the action probabilities are normalized by the probability of the most likely interaction. The
most frequent interaction becomes 100% likely, and the rest are scaled proportionately. Now the
system can recognize that the bar provides context for picking up drinks, and the counter
provides context for food. This works because it is the relative frequencies of interaction that are
important in clustering.
There are still some objects that do not cluster well due to lack of data, such as pots and pans,
blenders and food processors. This is not much of a problem, because the lack of data indicates
that these items are less significant to the scenario we are trying to learn.
The benefit of clustering is that the number actions in our action lexicon can now be greatly
reduced by referencing clusters rather than individual items. A clustered action is a new action
that has a number of children, where the child actions are the original unclustered actions.
Eating salmon at the table is clustered with eating steak at the table, as children of a new action
for eating food at the table. After 5,000 games the clustered action lexicon has 7,086 actions.
The maximum possible action lexicon size is now 31,014 actions, as calculated in Appendix B.
Coverage of the possibility space has doubled to over 20% (computed by dividing the observed
7,086 by the maximum 31,014). The graph below illustrates the benefit of clustering on action
lexicon size.
66
Size of Action Lexicon Over Time
12000
10000
Number of Actions
8000
Uncluste re d
6000
Cluste re d
4000
2000
0
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
Figure 4-17: Growth of action lexicon, clustered and unclustered, over 5,000 games.
4.2.4 Building a Language Lexicon
Building a language lexicon is a more straight forward process. As new words are encountered
while processing gameplay logs, these words are added to the language lexicon. After 5,000
games, the language lexicon has 23,691 words. The maximum language lexicon size is literally
infinite due to slang, typos, abbreviations, tenses, and multiple languages. However, the growth
is less than linear due to some limitation on the range of words people typically use in a
restaurant scenario.
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Size of Language Lexicon Over Time
25000
Number of Words
20000
15000
10000
5000
0
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
Figure 4-18: Growth of language lexicon over 5,000 games.
Table 4-2 details statistics about speech acts and words in 5,000 games. The number of words
per speech act is limited by the interface’s limit on the number of characters typed.
Speech Acts per Game (All)
Speech Acts per Game (Waitress)
Speech Acts per Game (Customer)
Words per Speech Act (All)
Words per Speech Act (Waitress)
Words per Speech Act (Customer)
Mean
40.28
20.71
19.72
4.07
4.16
3.97
Total Number of Speech Acts
244,845
Median
32
21
15
3
4
3
Mode
22
13
8
1
1
1
Max
438
271
320
31
21
31
Table 4-2: Speech act statistics, based on 5,000 games.
4.2.5 N-gram Models of Language and Behavior
N-gram models refer to a statistical modeling technique from the field of Natural Language
Processing. Borrowing this technique, and applying it to both the language lexicon and action
68
lexicon gives a means of estimating the likelihood of new gameplay sessions, and quantitatively
evaluating how well the learned model of restaurant behavior and language correlates with
humans’.
An n-gram model estimates the likelihood of a sequence of words of length N by counting the
frequency of this sequence in a training corpus, and dividing by the total number of unique ngrams. Unigrams estimate the likelihood of atomic words. Bigrams estimate the likelihood of
pairs of words. Trigram estimate triplets and so on. The likelihood of an entire sentence can be
estimated by iterating over every sequence of N words, and multiplying the n-gram probabilities
together. For example, estimating the likelihood of the sentence “The dog laughs.” with trigrams
looks like this:
P(the, dog, laughs) = P(the | START)
x P(dog | START, the)
x P(laughs | the, dog)
x P(STOP | dog, laughs)
The trigram P(laughs | the, dog) is the probability of seeing “laughs” given that we have already
seen “the dog”. Enclosing each sentence in START and STOP markers captures the probability
of starting or ending a sentence with a particular n-gram. In practice, instead of looking at the
product of probabilities, looking at the sum of the log probabilities keeps the likelihood from
going to zero. The above example becomes:
log P(the, dog, laughs) = log P(the | START)
+ log P(dog | START, the)
+ log P(laughs | the, dog)
+ log P(STOP | dog, laughs)
If all of the actions in an entire gameplay session are treated as one sentence, an n-gram model
can be used to estimate the likelihood of this sequence. Similarly, all of the words in all of the
speech acts in one gameplay session can be concatenated into one long sentence, to estimate the
likelihood of the sequence of words in one game. This provides two lenses through which new
gameplay sessions can be examined to assess their typicality. If the likelihoods assigned by the
action and language models correlate well with ratings of typicality by humans, then this
quantitatively shows that the learned Plan Network is a good representation of how humans think
about the restaurant scenario.
As illustrated by the figures this section, even after 5,000 games, the action lexicon and language
lexicon are still growing. Words could be clustered by lexical similarity, or by leveraging an
external source such as WordNet (1998), to gain similar benefits to those achieved in clustering
actions. This would decrease the rate of growth of the language lexicon, but would not eliminate
the growth. The probability calculations need to incorporate discounting and smoothing
techniques to counter the effects of data sparsity. In order to estimate the likelihood of
previously unseen action or word sequences or atoms, true counts are discounted in some way,
and the missing probability mass is distributed among previously unseen n-grams. I
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implemented Katz Back-Off smoothing as described in Jurafsky & Martin (2000) and Manning
& Schütze (1999).
The Katz method computes a discounted maximum likelihood estimate for bigrams that do exist
in the corpus, and backs-off to an estimate defined in terms of unigram maximum likelihoods for
previously unseen bigrams. In the Katz formula, Count* refers to the true count discounted by
subtracting some discount factor.
Define two sets:
A( wi !1 ) = {w : Count ( wi !1 , w) > 0}
B( wi !1 ) = {w : Count ( wi !1 , w) = 0}
The bigram model:
$
Count * ( wi &1 , wi )
!
Count ( wi &1 )
!
PKATZ ( wi | wi &1 ) = #
PML ( wi )
!( ( wi &1 )
!"
'w%B ( wi &1 ) PML (w)
where $ ( wi "1 ) = 1 "
If
wi % A( wi &1 )
If
wi % B( wi &1 )
Count * ( wi "1 , w)
w# A ( wi "1 ) Count ( wi "1 )
!
Figure 4-19: Katz back-off model for bigrams.
Similarly, the Katz method computes a discounted maximum likelihood estimate for existing
trigrams, and backs-off to an estimate defined in terms of Katz bigrams for previously unseen
trigrams. This back-off technique continues up to the desired length of n-grams, each backingoff to an estimate defined in terms of Katz n-grams one word smaller.
Define two sets:
A( wi ! 2 , wi !1 ) = {w : Count ( wi ! 2 , wi !1 , w) > 0}
B( wi ! 2 , wi !1 ) = {w : Count ( wi ! 2 , wi !1 , w) = 0}
The trigram model is defined in terms of the bigram model:
$ Count * ( wi & 2 , wi &1 , wi )
If wi % A( wi & 2 , wi &1 )
!
Count ( wi & 2 , wi &1 )
!
PKATZ ( wi | wi & 2 , wi &1 ) = #
( ( wi & 2 , wi &1 ) PKATZ ( wi | wi &1 )
If wi % B( wi & 2 , wi &1 )
!
!" ' w%B ( wi &2 , wi &1 ) PKATZ ( w | wi &1 )
where $ ( wi " 2 , wi "1 ) = 1 "
Count * ( wi " 2 , wi "1 , w)
!
w# A ( wi " 2 , wi "1 ) Count ( wi " 2 , wi "1 )
Figure 4-20: Katz back-off model for trigrams.
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Chapter 5
Evaluation, Results, and Discussion
I evaluated the system quantitatively by computing likelihood estimates for gameplay sessions
based on a combination of physical actions and words, and correlating these scores with human
ratings of the same sessions. The intuition is that if the system has learned a high quality Plan
Network for the restaurant scenario, it should exhibit a human-like ability to judge typical and
atypical behavior in the restaurant environment. The ability to judge typicality is a powerful
asset of a Plan Network that enables users of the system to automatically detect off-script
behavior; irregularities which would be undetectable with a hand crafted script. Behavior and
language estimated to be highly likely by the Plan Network represents the behavior and language
that can be expected from an agent driven by the learned model. Behavior and language
estimated to be unlikely provides a red flag, indicating that some action may be required on the
part of an agent to direct the scenario back towards typical behavior.
5.1 Tuning the System for Optimal Correlation
I randomly selected 200 log files that I set aside for validation and testing, and trained the system
on the remaining 5,000. I created a validation set by rating 100 of the set aside files on a one to
seven scale indicating how typical I felt the interaction was, with respect to typical restaurant
behavior and language in an English speaking country. Below are the guidelines I set for
evaluating typicality.
Rating Description
7 Perfect example of what you would expect to witness in a restaurant.
6 Pretty good example, with a couple of out of the ordinary events.
5 Generally what happens in a restaurant, with a handful of out of the ordinary events.
4 A fair amount of out of the ordinary behavior, while generally going through expected events.
3 Lots of out of the ordinary behavior, while generally going through expected events
2 Very little ordinary behavior for a restaurant
1 Not at all what people do in a restaurant
Table 5-1: Descriptions of typicality ratings given to raters.
I rated the games by reading the filtered log files, which look like the script illustrated in Figure
4-2, and assigning a single rating between one and seven. It took about six hours to rate 100
games. Below is a histogram of the ratings that I assigned.
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Histogram of Ratings for Validation Set
25
# of Gameplay Sessions
20
15
10
5
0
1
2
3
4
5
6
7
1 to 7 Ra ting of Typica lity
Figure 5-1: Histogram of human ratings for validation set.
I computed likelihood scores for games by building an n-gram action model from the action
lexicon and an n-gram language model from the language lexicon, based on the 5,000 games in
the training set. The n-gram models estimated likelihood scores with Katz Back-Off smoothing
and a constant discount factor. I interpolated between the scores from the action model and the
language model to find an overall likelihood estimate for each game. I ran a number of
experiments to find the best combination of action and word n-grams of various lengths, and to
select the discount factor. The goal of the experiments was to maximize the correlation between
the likelihood scores and my ratings for the 100 games. Correlation was measured by computing
the Pearson’s R correlation coefficient.
Figure 5-2 illustrates the effect of different discount factors on the correlation for various lengths
of n-grams of physical actions. I found that a discount of subtracting 0.5 from the true count
gave the best correlation over the variety of n-gram lengths. I used the discount factor of 0.5 in
all experiments from this point on in validation and testing.
72
Effect of Discount Factor on Correlation
0.57
0.55
Pearson's R
0.53
(Count - 0.1)
(Count - 0.3)
(Count - 0.5)
0.51
(Count - 0.7)
(Count - 0.9)
0.49
0.47
0.45
2-grams
3-grams
4-grams
5-grams
Action Mode l n-gra m s
Figure 5-2: Effect of discount factor on correlation.
I estimated the likelihood of each game three times for each length of action model n-grams.
First, I estimated the likelihood based on the interleaved model containing actions from both the
customer and waitress. Next, I estimated the likelihood based on the role-specific model for
each social role, once ignoring customer actions and next ignoring waitress actions. Figure 5-3
shows that averaging the likelihoods of the separate models leads to a better correlation than can
be achieved with the interleaved model. Much of the time the two actors are engaging in
behavior in parallel, so the interleaved model is subjected to a great deal of noise in the transition
probabilities between actions. The separate models identify strange behavior on the part of
either character, and averaging the likelihoods from each separate model yields the best overall
perspective on typicality. The best correlation found overall was R = 0.633, based on averaging
likelihoods of action model 4-grams from separate waitress and customer models.
73
Correlation Between Action Model Likelihoods and Human Ratings
0.7
0.6
Pearson's R
0.5
Interleaved Model
0.4
Waitress Model
Customer Model
0.3
Waitress & Customer Models Averaged
0.2
0.1
0
1-grams
2-grams
3-grams
4-grams
5-grams
6-grams
Interleaved Model
0.58316
0.45939
0.53593
0.5428
0.57502
0.54769
Waitress Model
0.5354
0.48681
0.47682
0.56411
0.54673
0.50171
Customer Model
0.39493
0.51289
0.50808
0.49638
0.4899
0.51587
Waitress & Customer
Models Averaged
0.53719
0.60382
0.59729
0.63305
0.61
0.60286
n-gra m s
Figure 5-3: Correlation between action model likelihoods and human ratings.
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Figure 5-4 contains the top 20 action 4-gram plan fragments for the waitress found in the
validation set, and Figure 5-5 contains the top 20 action 4-gram plan fragments for the customer.
Waitress 4-gram Plan Fragments
PUTDOWN_OldFood(Counter) EAT_OldFood(Counter) PICKUP_OldFood(Counter) PUTDOWN_OldFood(Counter)
PICKUP_OldFood(Counter) PUTDOWN_OldFood(Counter) EAT_OldFood(Counter) PICKUP_OldFood(Counter)
PUTDOWN_Beverage(Table) LOOKAT_MenuBox(Floor) PICKUP_Food(Counter) PUTDOWN_Food(Table)
EAT_OldFood(Counter) EAT_OldFood(Counter) EAT_OldFood(Counter) EAT_OldFood(Counter)
PICKUP_Flower(Table) PUTDOWN_Flower(Table) PICKUP_Food(Counter) PUTDOWN_Food(Table)
TOUCH_Fridge(Floor) PICKUP_DirtyDish(Table) EAT_DirtyDish(WAITRESS) PUTDOWN_DirtyDish(Counter)
PICKUP_DirtyDish(Counter) PUTDOWN_DirtyDish(Counter) EAT_OldFood(Counter) EAT_OldFood(Counter)
PICKUP_Beverage(Table) PUTDOWN_Beverage(Counter) PICKUP_DirtyDish(Table) PUTDOWN_DirtyDish(Counter)
PICKUP_DirtyDish(Table) PUTDOWN_DirtyDish(Counter) PICKUP_DirtyDish(Table) PUTDOWN_DirtyDish(Counter)
PUTDOWN_DirtyDish(Counter) PICKUP_DirtyDish(Table) PICKUP_Counter(Floor) PUTDOWN_DirtyDish(Counter)
PUTDOWN_DirtyDish(Counter) EAT_OldFood(Counter) EAT_OldFood(Counter) EAT_OldFood(Counter)
PUTDOWN_Food(Table) PUTDOWN_Menu(MenuBox) PICKUP_Food(Counter) PUTDOWN_Food(Table)
PICKUP_Food(Table) PUTDOWN_Food(Table) PICKUP_Food(Counter) PUTDOWN_Food(Table)
GIVE_Bill(Hand, UNPAID) PUTDOWN_Bill(Register, PAID) PICKUP_Menu(Table) PUTDOWN_Menu(MenuBox)
EAT_OldFood(Counter) EAT_OldFood(Counter) EAT_Food(Counter) EAT_Food(Counter)
TOUCH_Microwave(DishWasher) PUTDOWN_Flower(Table) PICKUP_Food(Counter) PUTDOWN_Food(Table)
PICKUP_DirtyDish(Table) PUTDOWN_DirtyDish(Counter) EAT_DirtyDish(Table) PICKUP_DirtyDish(Table)
EAT_Food(Counter) EAT_Food(Counter) EAT_OldFood(Counter) EAT_OldFood(Counter)
PICKUP_Beverage(Bar) PUTDOWN_Beverage(Floor) PICKUP_Food(Counter) PUTDOWN_Food(Table)
PICKUP_DirtyDish(Bar) PUTDOWN_DirtyDish(Counter) EAT_OldFood(Counter) EAT_OldFood(Counter)
Figure 5-4: Top 20 waitress 4-gram plan fragments.
Customer 4-gram Plan Fragments
EAT_Beverage(Table) EAT_Beverage(Table) PICKUP_Food(Table) PUTDOWN_Food(Table)
EAT_Food(Table) EAT_Beverage(Table) LOOKAT_Bill(Hand, UNPAID) PAY_Bill(Hand, PAID)
EAT_Chef(Floor) EAT_Fruit(DishWasher) EAT_FoodPrep(TrashCompactor) EAT_FoodPrep(TrashCompactor)
EAT_Fruit(DishWasher) EAT_FoodPrep(TrashCompactor) EAT_FoodPrep(TrashCompactor) EAT_FoodPrep(TrashCompactor)
PICKUP_DirtyDish(Table) PUTDOWN_DirtyDish(Table) PICKUP_Food(Table) PUTDOWN_Food(Table)
EAT_Beverage(Table) PICKUP_Food(Hand) PUTDOWN_Food(Table) EAT_Food(Table)
GIVE_Bill(WAITRESS, UNPAID) EAT_Trash(Hand) EAT_Trash(Hand) EAT_Trash(Hand)
EAT_Food(Table) EAT_Food(Table) PICKUP_Bill(Table, UNPAID) LOOKAT_Bill(Hand, UNPAID)
GETOFF_Chair(Floor) SITON_Menu(Table, Menu) GETOFF_Menu(Table, Menu) SITON_Chair(Floor)
EAT_DirtyDish(Counter) EAT_Microwave(DishWasher) EAT_FoodPrep(TrashCompactor) EAT_FoodPrep(TrashCompactor)
TOUCH_Fridge(Floor) TOUCH_Counter(Floor) EAT_FoodPrep(TrashCompactor) EAT_FoodPrep(TrashCompactor)
EAT_Beverage(Hand) PUTDOWN_DirtyDish(Table) PICKUP_Bill(Table, UNPAID) LOOKAT_Bill(Hand, UNPAID)
EAT_DirtyDish(Table) EAT_DirtyDish(Table) EAT_Food(Table) EAT_Food(Table)
EAT_OldFood(Counter) EAT_Wine(Bar) EAT_Wine(Bar) EAT_Wine(Bar)
EAT_Beverage(Hand) GIVE_DirtyDish(WAITRESS) LOOKAT_Bill(Hand, UNPAID) PAY_Bill(Hand, PAID)
EAT_DirtyDish(Table) EAT_DirtyDish(Table) EAT_Food(DirtyDish) EAT_Food(DirtyDish)
EAT_Counter(Floor) EAT_OldFood(Counter) EAT_OldFood(Counter) EAT_OldFood(Counter)
PICKUP_OldFood(Counter) EAT_OldFood(Hand) EAT_OldFood(Hand) EAT_OldFood(Hand)
LOOKAT_Food(Table) EAT_Beverage(Table) PICKUP_Food(Table) PUTDOWN_Food(Table)
EAT_Wine(Table) EAT_Beverage(Table) PICKUP_Food(Table) PUTDOWN_Food(Table)
Figure 5-5: Top 20 customer 4-gram plan fragments.
I repeated these experiments with the language model, and again found that averaging the
likelihoods from separate models yields a better correlation than that achieved by the interleaved
model. The average of the likelihoods from separate bigram models yields the best combined
correlation with R = 0.546. Interestingly, this is slightly lower than the best correlation for the
waitress-only bigram model, possibly indicating that the waitress’s speech is more informative
than the customer’s or their combined dialogue. However, due to the tiny discrepancy between
the two best R values, I chose to continue using the averaged bigram model for future
experiments to ensure that the customer speech was being taken into account.
75
Correlation Between Language Model Likelihoods and Human Ratings
0.6
0.5
0.4
Pearson's R
Interleaved Model
Waitress Model
0.3
Customer Model
Waitress & Customer Models Averaged
0.2
0.1
0
1-grams
2-grams
3-grams
4-grams
5-grams
Interleaved Model
0.4599342 0.5394825
0.4292749
0.4079744 0.3471428
Waitress Model
0.4571513 0.5493757
0.4597358
0.4410501 0.3553472
Customer Model
0.2982031
0.4236366
0.4705353 0.4674135
Waitress & Customer
Models Averaged
0.4197156 0.5463326
0.5303506
0.5377071 0.4874643
0.40004
n-gra m s
Figure 5-6: Correlation between language model likelihoods and human ratings.
76
Table 5-2 lists the top 40 most probable bigrams for waitresses and customers found in the
validation set. Note that punctuation has been removed, all words have been forced to
lowercase, and misspellings come directly from the data.
Waitress Bigrams
Customer Bigrams
folow me
excuse me
follow me
supposed to
supposed to
thank you
thank you
berry pie
excuse me
du jour
restrount plz
followed by
trying to
cobb salad
vegetable soup
grilled salmon
nectarine tart
vegetable soup
interest you
trying to
berry pie
nectarine tart
por favor
piece of
let me
may i
du jour
glass of
sort of
iced tea
cobb salad
veg soup
grilled salmon
let me
youd like
slice of
hold on
cob salad
anyhting else
id like
twilight zone
cup of
blueberry pie
white wine
white wine
shut up
red wine
de jour
anyting else
refuse to
would you
red wine
may i
thatll be
show you
allergic to
glass of
charge me
woud you
kind of
shut up
bring me
help you
cheese cake
main course
filet mignon
serving you
worked here
such a
ru
helo thar
meet you
anything else
main course
cup of
can i
calling the
going to
united states
cherry cheesecake
Table 5-2: Top 40 bigrams for waitresses and customers.
Next I ran experiments to investigate the effect of averaging the likelihoods from the action and
language models. The likelihoods for each model are themselves computed by averaging the
likelihoods of the separate role-specific models. Figure 5-7 illustrates that for any length of ngrams, averaging the action and language models leads to better correlation with human ratings
than either model alone.
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Correlation Between n-gram Model Likelihoods and Human Ratings
0.8
0.7
0.6
Pearson's R
0.5
Language Model
0.4
Action Model
Language & Action Models Averaged
0.3
0.2
0.1
0
1-grams
2-grams
3-grams
4-grams
5-grams
Language Model
0.41971563 0.54633257
0.53035064
0.53770708 0.48746426
Action Model
0.53719022 0.60382363
0.59728537
0.63305019 0.60999882
Language & Action
Models Averaged
0.59830767 0.67279676
0.68489742
0.71098948 0.67655025
n-gra m s
Figure 5-7: Correlation between n-gram model likelihoods and human ratings.
Having determined that averaging likelihoods from action model 4-grams, and language model
bigrams yields the best respective correlations with human ratings, and averaging action and
language models outperforms either model alone, my next goal was to find the best overall
combination achievable by interpolating between likelihoods for actions and language. Figure 58 details various combinations of actions and language, with the peak at R = 0.782 with 25%
physical actions and 75% words. The extents of the graph show the correlation based on only
language on the left and actions only on the right.
78
Correlation Between Interpolated Likelihoods Combining Actions and Language
and Human Ratings
0.8
0.7
0.6
Pearson's R
0.5
0.4
0.3
0.2
0.1
0
10
/0
0
/5
/1
5
5
0
/1
/2
0
5
/2
/3
5
0
/3
/4
5
0
/4
/5
0
5
/5
/6
5
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/7
5
0
/7
/8
0
/8
0
95
90
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70
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60
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50
45
40
35
30
25
20
15
5
/9
00
/9
/1
10
5
0
Proportion of Like lihoods: Actions / La ngua ge
Figure 5-8: Correlation between interpolated likelihoods combining actions and language and human ratings.
Figure 5-9 re-presents these results more clearly, challenging the old adage that actions speak
louder than words. On their own, the likelihoods estimated by the action model do correlate
better with human ratings than the language model, but the combination of the two correlates
significantly better, with an interpolation that favors the language model 75 to 25. This is an
interesting counter-intuitive result, given that Figure 5-7 shows the action model consistently
outperforming the language model.
79
Effect of Interpolating Action and Language Models on Correlation for the
Validation Set
0.8
0.7
0.6
Pearson's R
0.5
0.4
0.3
0.2
0.1
0
Correlation
Language
Action
0.546332566
0.633050188
Action and Language (25 / 75)
0.728200802
n-gra m Mode ls
Figure 5-9: Effect of interpolating action and language models on correlation for the validation set.
The final figure in this section renders the scatter plot of the relationship between likelihood
scores estimated by the best interpolated combination from above and human ratings. Pearson’s
R = 0.782 shows a strongly significant correlation for 100 games. With 100 games, a correlation
of .254 or greater is significant at the .01 level (p<.01).
80
Correlation Between Likelihoods and Human Ratings
0.14
0.12
Likelihoods
0.1
0.08
R = 0.7282
0.06
0.04
0.02
0
0
1
2
3
4
5
6
7
8
Hum a n Ra tings (7 is Most Typica l)
Figure 5-10: Scatter plot of correlation between likelihoods and human ratings.
Based on the scatter plot, it looks like 0.04 is a good threshold for discriminating between typical
and atypical gameplay sessions. Ignoring the eight games that I rated as neutral, and considering
anything above four to be typical, and below four atypical, the model’s binary classification
results are presented in Table 5-3.
Likelihood > 0.04 (Typical)
Likelihood <= 0.04 (Atypical)
Rating > 4 (Typical)
35 True Positives
8 False Alarms
Rating < 4 (Atypical)
11 False Positives
38 True Negatives
Table 5-3: Binary classification matrix comparing estimated likelihoods to human ratings.
If the likelihood threshold of 0.04 was used as the criteria for filtering which gameplay sessions
to include in the visualization graphs described in Chapter 4, 81% of games that I rated as typical
would be incorporated into the graphs, and 78% of atypical examples would be filtered out.
5.2 Testing the System
The evaluation demonstrated that the likelihood scores estimated by the Plan Network correlate
well with my ratings of 100 games. To test the system, I had 10 people who were not involved
with the development of the project rate 300 games. These ratings were compared to likelihoods
estimated using the best parameters found in the evaluation.
81
I randomly selected 200 gameplay log files from the training set to add to the 100 I had
originally set aside for testing, for a total of 300 test games. I kept the 100 games from the
validation set out completely so as not to introduce any bias during testing, and retrained a new
Plan Network with the remaining 4,800 games. None of the 300 games used for testing were
included in the 4,800 games used to train the new Plan network. Each of the ten raters received
40 games to rate on a one to seven scale of typicality. Everyone was given the same ten games
from the validation set, and a unique set of 30 games from the test set. I hand selected the ten
games from the validation set to ensure raters were given an even spread of typical and atypical
games. The overlapping ratings of validation set games gave me some means of ensuring that all
of the raters understood and followed instructions.
5.2.1 Inter-Rater Agreement
Figure 5-11 displays the ratings that each of the ten raters assigned to each of the ten games in
the validation set. Overall, the raters generally agreed within one unit of typicality. Game four
caused raters the most confusion due to people playing the game in a foreign language. Despite
explicit instructions to rate typicality based on expected behavior in a restaurant in an English
speaking country, rating varied widely from two to six. My own rating for this game was two.
Human Rater Agreement
8
7
Rater 1
6
Rater 2
Typicality Rating
Rater 3
5
Rater 4
Rater 5
Rater 6
4
Rater 7
Rater 8
3
Rater 9
Rater 10
2
1
0
1
2
3
4
5
6
7
8
9
10
Ga m e pla y Se ssions
Figure 5-11: Human inter-rater agreement.
I formally measured inter-rater agreement with the R statistical package’s implementation of
Light’s weighted Kappa for N raters. This is a metric of inter-annotator agreement between 0.0
82
and 1.0 for non-categorical, real valued ratings. I found Kappa = 0.8643515. I also computed
the average correlation between any pair of raters, and found the mean Pearson’s R = 0.899.
Rater Agreement with Me
8
7
Typicality Rating
6
5
My Rating
4
Mode of 10 Raters
3
2
1
0
1
2
3
4
5
6
7
8
9
10
Ga m e pla y Se ssions
Figure 5-12: Rater agreement with my ratings, based on mode of 10 rater’s ratings.
Figure 5-12 shows how well the ten raters agreed with my ratings. Based on the mode of ten
ratings for each game, the raters generally agree with my ratings. However, comparing the
histogram of ratings in Figure 5-13 to the histogram of my ratings in Figure 5-1 uncovers a
discrepancy in the handling of neutral ratings for games. I tended to rate games above or below
four, choosing not to rate games as neutral, while the other raters chose neutral most often. This
is a notable difference in the way games were rated during validation and testing that may have
contributed to a slight difference in the final correlation found between the system’s likelihood
rating and human ratings of typicality during validation and testing.
83
Histogram of Ratings for Test Set
60
# of Gameplay Sessions
50
40
30
20
10
0
1
2
3
4
5
6
7
1 to 7 Ra ting of Typica lity
Figure 5-13: Histogram of human ratings for test set.
5.2.2 Test Results
I computed the correlation between the human ratings for the 300 games in the test set, and the
likelihood scores estimated with the parameters found experimentally in the evaluation – the 25 /
75 combination of likelihoods from the action model and language model, with action 4-grams
averaged over social roles, and language bigrams averaged over social roles. The result was a
Pearson’s R = 0.576 correlation coefficient, which is strongly significant for 300 games. With
300 games, a correlation of .147 or greater is significant at the .01 level (p<.01).
84
Correlation Between Likelihoods and Human Ratings
0.14
0.12
Likelihoods
0.1
0.08
R = 0.5756
0.06
0.04
0.02
0
0
1
2
3
4
5
6
7
8
Hum a n Ra tings (7 is Most Typica l)
Figure 5-14: Scatter plot of correlation between likelihoods and human ratings.
The correlation in the test set is slightly lower than in the validation set, which can be attributed
to a number of causes. The test set is three times as large, and may have contained more
variation. The raters were unfamiliar with the data, and did not share my intimate knowledge of
the game and learning process. Regardless of these issues, there was still a strong correlation,
indicating that it is possible to learn a Plan Network from video game data that does reflect
human judgment of typicality. Figure 5-15 illustrates that the test set follows the same
progression of improvement when comparing correlation with the language model, the action
model, and the interpolated model.
85
Effect of Interpolating Action and Language Models on Correlation for the
Test Set
0.7
0.6
Pearson's R
0.5
0.4
0.3
0.2
0.1
0
Correlation
Language
Action
0.441504932
0.503101381
Action and Language (25 / 75)
0.575601716
n-gra m Mode ls
Figure 5-15: Effect of interpolating action and language models on correlation for the test set.
If the likelihood threshold of 0.04 is applied to the test set, Table 5-4 details the system’s
performance on the binary classification task of identifying typical and atypical gameplay
sessions. The Plan Network correctly identifies 75% of the games that humans rated as typical,
and filters out 75% of the games that humans found to be atypical.
Likelihood > 0.04 (Typical)
Likelihood <= 0.04 (Atypical)
Rating > 4 (Typical)
99 True Positives
33 False Alarms
Rating < 4 (Atypical)
32 False Positives
85 True Negatives
Table 5-4: Binary classification matrix comparing estimated likelihoods to human ratings.
5.3 System Successes and Failures
Figure 5-16 is a graph visualization of customer behavior in a game rated as a seven by a human,
with a high likelihood estimate from the system (above 0.1). The player’s actions are
highlighted by the blue nodes, connected with bold edges. The player’s actual behavior follows
an existing path in the customer Plan Network.
86
87
Figure 5-16: Graph of customer behavior in a game rated typical by humans and the system.
The graph only represents the physical behavior. It is possible to find games where the physical
behavior is typical, but the language is not, and the system can successfully identify these as
well. Figure 5-17 contains an excerpt from a game where the waitress and customer engage in
ordinary behavior such as eating food and paying bills, but the dialogue between them reveals
that the customer is trying to leave without paying, and trying to take the waitress home with
him. Here the human rating is two, and the system estimates a likelihood of below 0.03. The
typicality of physical behavior is estimated as highly likely with a score over 0.09, but the
unusual language forces the combined likelihood below the threshold of typical behavior. In a
similar manner, games played in a foreign language can be easily identified. The system is also
sensitive to out of the ordinary word ordering, allowing detection of games containing broken
English.
88
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WAITRESS: "welcome!!"
CUSTOMER: "i wanna eat some food"
CUSTOMER: "i havent eaten in eight days"
WAITRESS: "have a sit... and enjoy!"
CUSTOMER: "stupid heroin addiction"
CUSTOMER WALKS TO table1
WAITRESS GIVES Menu TO CUSTOMER
CUSTOMER LOOKSAT Menu
WAITRESS: "hungry ah?"
CUSTOMER: "gimme a filet mignon and six beers"
WAITRESS: "six???"
CUSTOMER: "yes i've had a very bad day"
CUSTOMER GIVES Menu TO WAITRESS
WAITRESS: "well.. ok... but it's better to drink one at a time..."
CUSTOMER: "what are you, my mother"
WAITRESS: "or they'll get warm!!!"
...
39777071
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39808024
39829117
39833649
39856274
39865242
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[CONVERSATION BETWEEN CUSTOMER AND WAITRESS]
WAITRESS: "are leaving so fast???"
CUSTOMER: "i'm an american baby, I do whatever I want"
WAITRESS: "ok... but I thought... you... would want me to"
WAITRESS: "come with you for a walk after work..."
CUSTOMER: "I'll give you a ''tip,'' if thats what you're asking for"
WAITRESS: "well... to be honest... you still have to pay the bill!"
CUSTOMER: "lets go back inside so you can get paid, then you come home with m"
CUSTOMER WALKS TO podium
39881071 WAITRESS: "ok... wait a minute..."
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WAITRESS TOUCHES Register
Bill APPEARS ON Podium
ITEM0="FILET MIGNON"
ITEM1="LOBSTER THERMADOR"
ITEM2="BERRY PIE"
ITEM3="BEER"
39922352
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WAITRESS PICKSUP Bill
WAITRESS GIVES Bill TO CUSTOMER
CUSTOMER UNDERPAYS Bill BY $0.29
CUSTOMER GIVES Bill TO WAITRESS
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39964852
39965992
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39990217
WAITRESS: "I think it's ok!"
CUSTOMER: "look lady, you're hot to trot, and we're going to shower together now"
WAITRESS: "you've been very kind..."
WAITRESS PUTSDOWN Bill ON Register
WAITRESS EARNS $25.28
40054992 WAITRESS: "moron!!!! I'm not coming with you..."
40081321 WAITRESS: " it was a trick to convince you to PAY!!!"
Figure 5-17: Game with typical physical behavior, but atypical language.
89
Figure 5-18 illustrates the Plan Network detecting off-script behavior in a game rated as a three
by a human, and below 0.04 by the system. The customer stands up after looking at the menu,
then sits down again. Even more alarming, the customer picks up the flower from his table, and
then grabs the cash register before exiting the restaurant (without paying his bill)! The off-script
actions and decisions are illustrated with red nodes and edges inserted manually into the
automatically generated visualization.
90
91
Figure 5-18: Graph of customer behavior in a game rated atypical by humans and the system.
The figures above demonstrate system successes, however relying on statistics and Markovian
assumptions will not always lead to successful detection of typical behavior. There are a number
of ways to confuse the system. Sometimes players engage in conversation about topics outside
of the scope of the scenario, introducing new vocabulary in an otherwise typical play through.
The unrecognized words lead the system to assign a low likelihood to a game that humans rate as
typical. Other times, players repeat typical restaurant language and behavior an atypical number
of times.
7790428 WAITRESS: "cherry pie"
7791428
Pie APPEARS ON Counter
7793398
7796368
7799388 [CONVERSATION BETWEEN CUSTOMER, WAITRESS, AND CHEF]
7799388 CUSTOMER: "big fun. How about beer?"
7805449 WAITRESS: "Sure"
7806563
7808035
WAITRESS WALKS TO bar
CUSTOMER WALKS TO bar
7809897 [CONVERSATION BETWEEN WAITRESS AND BARTENDER]
7809897 WAITRESS: "beer"
7810897
Beer APPEARS ON Bar
7814023 [CONVERSATION BETWEEN CUSTOMER, WAITRESS, AND BARTENDER]
7814023 CUSTOMER: "beer"
7815023
7816004
Beer APPEARS ON Bar
Beer APPEARS ON Bar
7816252 [CONVERSATION BETWEEN CUSTOMER, WAITRESS, AND BARTENDER]
7817252
Beer APPEARS ON Bar
Figure 5-19: Waitress and customer order lots of pie and beer.
Figure 5-19 displays an excerpt from a game where the waitress and customer cooperatively fill
the restaurant with pie and beer. The human rater rated this game a two, but the system’s failure
to assign a low likelihood supports Chomsky’s observation (1957) that statistics can never fully
capture the potentially infinitely recursive nature of language. The action model fails to
recognize this odd behavior due to the fact that the repeated physical behavior is actually
performed by the staff, placing items on the counter and bar, but the Plan Network only models
the customer and waitress. Even if the Plan Network incorporated a model of the staff, ordering
several dishes or beverages is not out of the ordinary, but ordering the same item multiple, times
is. This is one case where the loss of precision due to clustering leads to a false positive.
92
The Plan Network also has trouble with games that are atypically short or truncated. Figure 5-20
contains an entire script for a short game.
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WAITRESS APPEARS ON Floor
WAITRESS PUTSDOWN Menu ON MenuBox
CUSTOMER APPEARS ON Terrain
WAITRESS PICKSUP OLD_Salad_Bite3
WAITRESS WALKS BETWEEN counter AND table2
WAITRESS PUTSDOWN OLD_Salad_Bite3 ON Counter
CUSTOMER WALKS BETWEEN table1 AND table4
OLD_Salad_Bite3 DISAPPEARS
WAITRESS PICKSUP table4
10349884 [CONVERSATION BETWEEN CUSTOMER AND WAITRESS]
10349884 CUSTOMER: "hello?"
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WAITRESS PICKSUP OLD_Bill
WAITRESS PUTSDOWN OLD_Bill ON Register
WAITRESS EARNS $21.05
OLD_Bill DISAPPEARS
WAITRESS: "hi sir, can i get you a drink?"
CUSTOMER: "hello?"
WAITRESS: "can i get you a drink?"
CUSTOMER: "no!"
WAITRESS: "nice one"
CUSTOMER STANDSUP FROM chair3
CUSTOMER WALKS TO ExitTrigger(ExitTrigger)
Figure 5-20: Complete script for a very short game.
A human can recognize that this is not a typical restaurant experience, even though the actions
and words it is composed of are typical. Humans evaluate games based on both what did and
what did not happen, and they are aware that this game is strange because the customer refused
service and exited the restaurant without ordering, eating, or paying a bill. The system needs
some representation of the typical structure of a game to recognize that it is out of the ordinary
for a customer to just stand up and exit without having a meal. The current Plan Network
implementation only has access to a local representation of the expected sequence of events. It
cannot identify gross absences.
93
Chapter 6
Contributions and Future Work
This thesis has demonstrated that it is possible to not only collect a large quantity of data with a
multiplayer video game, but also data of high quality that accurately reflects typical human
behavior and language. A methodology has been described to learn a Plan Network from this
data that can be visualized as a graph, and interactively browsed to view conversations clustered
by context. A quantitative measure has been applied to a random selection of 300 games,
positively correlating the Plan Network’s estimate of likelihood with ratings from ten humans.
The Restaurant Game was quickly constructed for data collection purposes, and is hardly a game
at all. Yet, this restaurant simulation provided enough entertainment to capture behavior from
over 5,000 people. With hundreds of thousands of people online in Second Life, and millions
playing The Sims and World of Warcraft, the potential for using games to learn about humans is
enormous.
The data analysis performed so far only scratches the surface of a rich research area. The system
failures in Chapter 5 illustrate the need for additional representations of learned behavior that
capture the large scale structure of the narrative, possibly in the form of a plan grammar. A
context-sensitive grammar may be able to detect games with atypical omissions of behavior,
such as exiting the restaurant without paying the bill.
Even a perfect representation of the scenario learned from The Restaurant Game will not fully
reflect all that humans know about restaurants. The Plan Network only provides representations
of observable intentions, such as eating food, and paying bills. The data provided from log files
does not explicitly indicate whether the customer left a large tip because he was trying to impress
the waitress, or was just happy with his experience. A more robust model would incorporate
knowledge from other sources as well; perhaps transcripts from films and television shows, or
text from web sites about restaurants. These additional sources of knowledge could allow the
system to make inferences about unobservable intentions.
This thesis began with the statement that conversation is a collaboration, yet the best likelihood
estimations are achieved by learning separate models for the two social roles. Ultimately, an
additional unified model of the interaction dynamics between the customer and waitress is
desirable, because an agent who’s behavior is driven by the Plan Network will need to
understand when to expect the other actor to take a turn. The nodes of the Plan Network already
contain the knowledge required to build the unified model, in the form of action preconditions.
For example, the customer’s behavior model includes an edge from looking at the menu to eating
food. In between, a waitress must put food on the table for the customer to eat. The
preconditions of eating food require food to be on the table, providing the required knowledge
that interaction between the waitress and customer is required between looking at the menu and
94
eating food. Implementation of the unified model of interaction dynamics remains for future
work. Ideally the Plan Network will also scale to handle more than two simultaneous actors.
Despite the issues raised above, the Plan Network in its current form is already useful. Plan
Networks allow humans to visualize norms that emerge from thousands of people interacting in
the same environment, and may expose unexpected behavior or user interface confusion. A
system that can predict and suggest socially appropriate actions and utterances could open the
door to new types of communication aids, assisting people with Autism or other neurological
disorders affecting social behavior. The ability to recognize atypical behavior and language not
only enhances visualizations, but can also filter data used to teach an agent appropriate behavior,
and to recognize off-script behavior in real-time. Perhaps the chef starts a small fire in the
kitchen to distract a player who was detected taking the scenario in an atypical direction with
erratic behavior or out of vocabulary language. Course correction like this may improve games,
but would be especially useful in training simulations; one of the many potential application
areas for socially aware, conversational role-playing agents. In the future, conversational agents
powered by Plan Networks may provide new experiences in gameplay and training, allow new
ways to practice foreign languages, and act as digital extras in animated films and machinima.
Of course, there is more work to be done before Plan Networks can drive agents in interactive
applications. A future goal for this work is to automate conversational characters in a new
single-player restaurant game, fulfilling the promise made to participants on the project web
page.
95
Appendix A
Clusters
Below are the clusters found with the method described in section 4.2.3. The cluster names in
brackets after the items are assigned manually by inspecting the automatically generated clusters.
These labels are only used for visualization purposes.
Items that are created after the game starts are tagged as dynamic objects with the addition of
“.dyn” to their names. Note that the restaurant always starts in the same state, with one dirty
table covered with half eaten salad, salmon, beer, and wine, and a paid bill. Also, an old pie and
lobster are always sitting on the counter when the game starts.
This clustering technique does a good job of clustering the objects most commonly interacted
with in the restaurant scenario: food, drinks, and dirty dishes. This technique also successfully
distinguishes between the half eaten salad, salmon, beer and wine that are already on the dirty
table from fresh items ordered from the chef.
CLUSTER0=Brandy,Wine,Bottle [Liquor]
CLUSTER1=DishWasher [DishWasher]
CLUSTER2=Stove [Stove]
CLUSTER3=Bar [Bar]
CLUSTER4=Stool,Chair [Chair]
CLUSTER5=Cuisinart [Cuisinart]
CLUSTER6=FoodPrep [FoodPrep]
CLUSTER7=Microwave [Microwave]
CLUSTER8=Pan [Pan]
CLUSTER9=Pot [Pot]
CLUSTER10=Fridge [Fridge]
CLUSTER11=TrashCompactor [TrashCompactor]
CLUSTER12=Table [Table]
CLUSTER13=Lobster,Pie [OldFood]
CLUSTER14=Counter [Counter]
CLUSTER15=Trash [Trash]
CLUSTER16=Podium [Podium]
CLUSTER17=Fruit [Fruit]
CLUSTER18=Flower [Flower]
CLUSTER19=Blender [Blender]
CLUSTER20=Register [Register]
CLUSTER21=Chef [Chef]
CLUSTER22=Bartender [Bartender]
CLUSTER23=Salmon,RedWine,Salad,Beer,Glass,Plate,WineGlass,Bowl,TeaCup,Mug [DirtyDish]
CLUSTER24=Bill [OldBill]
CLUSTER25=Interior [Interior]
CLUSTER26=WAITRESS [WAITRESS]
CLUSTER27=CUSTOMER [CUSTOMER]
CLUSTER28=Menu.dyn [Menu]
CLUSTER29=Vase [Vase]
CLUSTER30=Filet.dyn,Spaghetti.dyn,Salmon.dyn,Salad.dyn,Lobster.dyn,Soup.dyn,
Cheesecake.dyn,Tart.dyn,Pie.dyn [Food]
CLUSTER31=Beer.dyn,RedWine.dyn,Water.dyn,WhiteWine.dyn,Tea.dyn,Coffee.dyn [Beverage]
CLUSTER32=Wine_Empty [Wine_Empty]
96
CLUSTER33=Bill.dyn [Bill]
CLUSTER34=Trash_Empty,[Trash_Empty]
CLUSTER35=Fruit_Bowl [Fruit_Bowl]
CLUSTER36=Terrain [Terrain]
CLUSTER37=FoodPrep_Empty [FoodPrep_Empty]
CLUSTER38=Brandy_Empty [Brandy_Empty]
CLUSTER39=Bottle_Empty [Bottle_Empty]
97
Appendix B
Maximum Action Lexicon Size
This appendix details the calculation of the total number of possible combinations and
permutations of action types, roles, and variable assignments that define the maximum size of the
action lexicon. This process is repeated for unclustered and clustered actions.
The full list of objects is available in Appendix A. These objects can be divided into groups
based on whether they are consumable, movable, or immovable. Consumable objects change
shape with each bite, and can also be moved.
Players may take nine different actions: PickUp, PutDown, Give, SitOn, GetOff, LookAt, Eat,
Touch, and Pay. PutDown and Give only apply to objects that can be picked up. Pay only
applied to bills. The other six actions may be applied to any object.
B.1 Unclustered Actions
The restaurant has a total of 28 consumable objects, 50 moveable objects, and 14 immovable
objects. Any non-dynamic object may be treated as a surface to put something down on top of.
There are 50 possible surfaces.
Variations of PutDown and Give:
Moveable: 2 roles x 50 objects x 50 surfaces = 5,000
Consumable: 2 roles x 28 objects x 50 surfaces x 3 states = 8,400
Total number of actions: 2 x (5,000 + 8,400) = 26,800
Variations of PickUp, SitOn, GetOff, LookAt, Eat, and Touch:
Immovable: 2 roles x 14 objects = 28
Moveable: 2 roles x 50 objects x 50 surfaces = 5,000
Consumable: 2 roles x 28 objects x 50 surfaces x 3 states = 8,400
Total number of actions: 6 x (28 + 5,000 + 8,400) = 80,568
Variations of Pay:
Bills: 2 roles x 50 surfaces = 100
Total number of possible actions:
26,800 + 80,568 + 100 = 107,468
The system observed 11,206 actions in 5,000 games, which covers 10.43% of the possibility
space.
98
B.2 Clustered Actions
Once objects are clustered, there are 9 clusters of consumable objects, 25 clusters of moveable
objects, and 13 clusters of immovable objects. There are 37 clusters for possible surfaces.
Variations of PutDown and Give:
Moveable: 2 roles x 25 clusters x 37 surface clusters = 1,850
Consumable: 2 roles x 9 clusters x 37 surface clusters x 3 states = 1,998
Total number of actions: 2 x (1,850 + 1,998) = 7,696
Variations of PickUp, SitOn, GetOff, LookAt, Eat, and Touch:
Immovable: 2 roles x 13 clusters = 26
Moveable: 2 roles x 25 clusters x 37 surface clusters = 1,850
Consumable: 2 roles x 9 clusters x 37 surface clusters x 3 states = 1,998
Total number of actions: 6 x (26 + 1,850 + 1,998) = 23,244
Variations of Pay:
Bills: 2 roles x 37 surface clusters = 74
Total number of possible actions:
7,696 + 23,244 + 74 = 31,014
The system observed 7,086 clustered actions in 5,000 games, which covers 22.85% of the
possibility space. The coverage of the possibility space has more than doubled by clustering.
99
Appendix C
Learned Language Lexicon
This appendix contains the complete learned language lexicon, sorted by word frequency.
Words have been forced to lowercase, and punctuation has been stripped.
[4649] you [4596] i [4543] a [4198] the [4192] please [3980] have [3918] to [3731] and [3434] like [3403] can [3284] me [3212] is [3189] for
[3114] your [3010] would [3006] yes [2971] what [2965] no, thank [2956] hello [2914] are [2888] get [2839] that [2837] of [2780] it [2743] ok
[2597] thanks [2571] im [2500] here [2453] do [2441] my [2435] ill [2430] sir [2383] wine [2376] be [2329] beer [2286] menu [2281] good
[2255] this [2251] drink [2237] with [2187] anything [2112] just [2080] else [2057] on [2029] right [2005] water [1991] there [1987] how [1982]
go [1975] will [1960] one [1946] bill [1932] in [1926] some [1902] soup [1880] take [1875] sure [1871] hi [1825] check [1819] sorry [1789]
want [1784] table [1770] not [1738] was [1737] now [1726] so [1658] seat [1656] welcome [1615] dont [1592] red [1552] up [1547] nice [1540]
its [1532] sit [1516] salad [1513] or [1475] oh, well [1459] all [1390] come [1370] very [1365] glass [1350] but [1328] we [1325] if [1321] order
[1315] more [1308] lobster [1299] back [1276] may [1273] see [1260] eat [1256] pie [1238] where [1219] u [1202] know [1200] day [1191] then
[1187] thats [1173] something [1169] too [1166] bye [1165] at [1160] am [1136] white [1113] think [1079] did [1068] great [1064] need [1056]
could [1045] out [1040] ready [1037] much [1036] another, from [1034] start [1025] about [1014] pay [1010] again [982] hey [976] filet [971]
dessert [960] coffee [955] spaghetti [954] follow [952] any [950] today [906] salmon [901] let [898] youre [894] enjoy [874] coming [872] give
[871] tart [866] okay [842] time [834] fine, food [815] cheesecake [808] way [800] why [791] steak [789] lol [773] yeah [765] problem [760]
away [753] course [752] look [743] cobb [730] down [722] mignon [717] tea [705] our [697] as [695] meal [689] help [688] off [685] when [665]
id [651] didnt [646] berry [639] house [638] really [634] got [620] bring [615] first, going [610] evening [606] name [598] an, thing [590] hope,
were [586] done [583] flowers, had [582] waitress [580] moment [579] cant [552] by [547] tip [544] whats [533] grilled, nectarine [531] alright,
only [527] over [524] free [521] jour [519] money [515] wait [506] chef [505] make [504] try [496] tell [492] lets [491] say [485] vegetable [484]
should [482] everything [479] looks [476] been [460] ya [455] put [453] specials [452] maybe [446] delicious [444] restaurant [440] they [436]
du [431] leave [430] haha [424] hungry, wow [417] cake [415] cool [414] excuse [413] dinner [411] ah, heres [410] cherry, desert [402] game
[399] anywhere [394] ive [392] love [390] ask [387] excellent [382] little [377] also, better, kind [373] doing [367] bit, sounds, work [363] old
[361] hear [358] guess [357] soon [356] mean [354] place, though [353] he [350] choice [346] tonight [345] new [340] service, special [339]
hmm [335] before [334] them [333] mind [332] certainly [331] main [325] still, ur [320] call [319] actually [318] while [317] night [315] must,
said [314] marinara [312] o [310] bar [308] r [307] thankyou [305] other [303] long [301] yourself [300] customer, theres [298] two [293] cheese
[288] care [287] shall, um [286] thought, yet [282] after, pretty [280] lovely [279] man [278] finished [277] these [275] pick [273] bad [268]
wanna [265] quite [263] recommend [262] around, never [261] has, perhaps [260] minute [253] job [252] goodbye [251] cup [247] fun, real,
wont [245] window [241] does [240] lot, nope [239] best, register [237] miss [236] ate, k [234] later [233] spagetti, those [232] second [230]
wonderful [229] d, isnt [227] even, find [226] already, youd [224] who [223] last, people [222] big, bitch, us [221] de [220] yea [219] enjoyed,
gonna [218] bottle [216] many [215] might, use [214] serve [213] plate [211] paid, than [210] eating [209] enough, hehe, next [208] mmm [207]
ever, girl [206] made, nothing, talk [205] cash, coffe [204] beers [202] guy, hot, outside [201] trying [200] plz [199] feel [197] myself [196]
bartender, stop [195] tasty [194] always, huh, show [193] play [192] wrong [191] doesnt [190] few [187] home [186] ha, same [185] maam [184]
anyway [182] fuck [181] live [179] sweet [178] customers, having [177] forgot [176] because, drunk, glad [175] life [174] fruit, oops [173]
funny, getting [172] full, hmmm, uh, wanted [171] ass, looking, things [170] decided, havent [169] him, indeed, working, yo [168] keep, meet, y
[167] hows, left, starter [165] thermador, yep [164] refill, wish [163] youll [162] bite [161] drinks [160] join [158] wat [157] paying [155] which
[154] boy [153] p, pleasure, umm, youve [152] finish [150] being, stuff, yours [149] hold [148] fresh [147] charge, seem [146] mine [145] clean,
dear, t [144] afraid, move [143] dishes, offer, ordered, s, someone [142] gave, yummy [140] cheers, stay [139] ahh [137] believe, eh, hard, hurry
[136] gotta [135] seems [134] into [133] god, yup [132] taste, wheres [131] cold, w [130] speak [129] baby, trash [128] slow [127] arent, fillet,
nah, todays [126] hahaha, run [125] empty, his, most [124] fast, talking [123] behind [122] choose, hell, since [121] person [120] head, thx [119]
busy, damn [118] board, both, mam, starters, walk [117] playing, sec [116] fish, leaving [115] perfect, prefer, whatever [114] cheescake, idea,
once, waiting [113] clear, cook, happy, worry, yum [112] awesome, hes, read, shit [111] mmmm, watch [110] compliments, minutes [109]
sitting, wasnt [108] english, ouch, understand, view, wherever [106] asked, grab, three, told, took [105] vase, whenever [104] chair, far [103]
ahead, catch [102] heard, remember, seated, used, waiter, whine [101] flower, kitchen, says, sex, such [100] brandy, decide, glasses, pasta, started
[99] rather, rude, theyre [98] crazy, e, interesting, m, own, years [97] buy, gimme, probably, suggest, tables, wouldnt [96] asking, forget [95]
breakfast, her [94] friend, quick, survey [93] beautiful, instead, taking, without [92] lady, she, weird [91] afternoon, couldnt, microwave, woman
[90] shortly, tried [89] bet, extra, question, rest [88] came, either, face, fantastic [87] open, server, thatll, through [86] bon, c, drinking, every,
male, pleas, ty [85] dude, piece, police, went [84] cheque, friends, hand, kinda [83] alrighty, bowl, cute, thirsty [82] ago, alone, cause, end, room
[81] heh, kidding, type [80] suppose, true [79] business, dollars, female, garbage, makes, set [78] bathroom, chance, floor, hit, interest, meat, part,
year [77] bout, close, eaten [76] appetizer, lunch, morning, near, touch [75] b, change, counter, eyes, favorite, hate, school, stupid, wall [74]
entree, guys, n, serving, town, ummm, vodka, world [73] anyone, ow, student, tastes, till, xd [72] almost, inside, sound, win [71] absolutely,
computer, date, mistake, played, road, ta, texas [70] break, desserts, gone, hon, ice, sexy [69] anymore, bored, days, dish, least, lots, manager,
ohh, side, thanx, trouble [68] add, babe, front, ooh, st, suck, wtf [67] age, dirty, england, fucking, hurt, pardon, seen, times [66] alot, fill, quiet,
stand, supposed, whole [65] correct, er, expensive, meant, medium, rare, square, staff, starving [64] ahhh, beverage, college, drive, number, pls,
saw [63] cream, deal, fat, kids, late, oi, omg, ones, poor [62] gets, hair, high, party, pass, repeat, shame, strange, their, usually, wines [61]
followed, kill, liked, madam, missed, self, silly, top, wife [60] broke, click, comes, meeting, often, tree, yay [59] alcohol, goes, la, lost, phone, th,
until [58] appreciate, aww, chat, found, heavy, hm, lobsters, saying, slice [57] aint, charged, dining, erm, honey, luck, matter, shouldnt [56] boss,
brb, cannot, die, shift, sometimes [55] boring, different, games, hands, kk, mmmmm, possible, week, young [54] asshole, called, case, cheap,
everyone, french, juice, making, plates, rush, wash, word, worked [53] allright, california, cooked, each, mr, resturant, under, usa, veggie, visit
100
[52] black, careful, country, crap, drank, fair, green, hang, hehehe, ll, problems, small, sucks, sweetie, thar, vegtable [51] allergic, beef, cob, dead,
fancy, kiss, married, payed, terrible, toast [50] amazing, coke, complimentary, cya, exactly, finest, horrible, il, mm, point, prob, si, sire, sort,
straight, uk, unfortunately [49] able, car, chefs, chicken, easy, happen, means, non, pies, round, sup, thinking, unless [48] bastard, cofee, helo,
men, objective, questions, re, recomend, soda, sometime, stuffed, together [47] don, giving, l, milk, mother, np, served, weve, whiskey [46]
afford, appetit, biting, champagne, city, cut, driving, martini, mom, return, stuck, sugar [45] anyways, apologize, bills, cops, dick, fly, liking,
ofcourse, quit, tomorrow, treat, wonder, worries [44] allison, apologies, com, comming, dance, dog, door, dunno, enjoying, family, half, living,
ring, turn [43] brought, da, five, gay, handsome, ho, min, names, ran, remove, restroom, running, sick, somewhere, stole, suit [42] agree, along,
anytime, bucks, canada, company, g, gift, hah, hahahaha, met, ohhh, oo, seeing, shut, steal, uhh, weather [41] nd, nevermind, oooh, outta, sry, tab,
tarts, teh, ugly [40] booze, buddy, feeling, figure, knew, lemme, odd, plus, whoa [39] aw, bear, body, burp, fact, fired, generous, happens, howdy,
noticed, okey, prepare, reccomend, sad, spot, whoops, wondering, yellow [38] boyfriend, card, comin, doin, je, lmao, looked, mingon, mister,
plese, serious, share, shes, single, smells, speaking, studying, teach, tips, women, write [37] answer, closed, cost, couple, dine, feed, girlfriend,
girls, goodnight, greetings, hahah, liek, magic, minon, owe, pants, short, street, tanks, text, worth, yah [36] although, apple, area, butt, chocolate,
dare, definitely, eggs, four, happened, hun, jk, less, light, ma, mate, needs, somethin, step, te [35] allowed, btw, calling, diner, hmmmm, honest,
itll, joke, london, middle, needed, oke, send, trick, virtual [34] act, berr, complete, em, forward, goin, husband, idiot, lue, none, rich, smoking,
soo, strong, thief, tired, wink, works, yeh [33] cafe, earlier, earn, f, listen, ou, sauce, sign, son, standing, taken, thermidor, wants, waters, zou [32]
allow, delightful, dumb, edible, err, establishment, fire, fix, h, kindly, language, moved, personal, price, trust, veg, watching, wot, ye [31]
apparently, appetite, blender, choices, email, especially, fridge, heart, hiya, hour, lucky, madame, okie, orange, seriously, teacher, terribly, ticket,
touched, tough, vegatable [30] accident, american, bee, boo, carry, chop, cutie, dat, elementary, eles, jesus, joe, joking, learn, north, notice, pleae,
sat, secret, sigh, smile, spend, srry, study, surely, wallet, warm, wil [29] available, between, darling, dollar, dropped, duh, except, expect, hallo,
knows, large, likes, marry, menus, mess, mit, msn, music, older, plan, polite, reservation, restaurants, sald, samon, selection, sleep, telling, ther,
throw, x, yer, york [28] alcoholic, beeer, besides, bin, brazil, corner, depends, fabulous, feet, floating, form, glas, hav, hide, iam, interested, jerk,
merlot, migon, mouth, pee, ru, state, stealing, waitresses, werent [27] across, aim, bob, bother, comfortable, confused, cuz, florida, flying, fool,
fuckin, grade, health, lie, lonely, merci, ordering, pleasant, smell, test, tho, toots, tutorial [26] ahhhh, america, awww, begin, children, gives,
hahahahaha, huge, imagine, months, moving, obviously, ot, places, refills, safe, save, scared, shot, sister, smart, teeth, turned, uhm, vegetarian,
waitressing, wats, woah, words [25] air, appears, aswell, bed, boobs, ciao, conversation, diet, double, fit, france, given, goodness, gosh, insist,
items, kid, local, mood, occupation, prepared, sense, shoot, sims, somthing, super, sweetheart, university, whisky, whit, whos [24] ahem, blind,
blue, burger, cents, dokey, education, experience, favor, however, nasty, nooo, peace, pei, personally, plant, quickly, reason, rock, six, sooo, stick,
takes, vegetables, wear, yu [23] anybody, australia, banana, basket, belgium, ber, bought, box, bread, brother, character, cheapskate, child, closer,
cmon, cock, cups, doctor, fucker, germany, gin, hic, holy, hopefully, human, important, lap, longer, lookin, month, opps, owner, paper, reach,
redwine, seconds, sell, shout, skip, soups, starting, toilet, ugh, un, visiting, wearing, whore [22] aha, blood, bloody, blush, bug, changed, chez,
chose, clothes, cunt, curious, dash, discount, disgusting, earned, eye, feels, freak, fruits, furniture, hola, holding, internet, jack, joining, jus, kick,
killed, knife, laugh, loved, low, machine, mad, movie, normal, nowhere, nuts, online, ooo, pizza, pm, popular, putting, que, robbery, salade,
screw, se, sink, specialty, spell, star, thatd, third, tonic, total, totally, voila, yr [21] agian, barman, birthday, coffie, count, definately, delay,
delicous, deserts, during, early, farewell, finally, foods, friendly, future, gratuity, hee, helping, heya, highly, hours, italian, italy, james, john, legs,
list, lord, management, neat, nobody, penis, pudding, rid, sam, sing, speghetti, surprise, tasted, touching, tu, ure, whatll [20] accent, adios, al,
balls, button, byebye, cab, chairs, checking, cocktail, compliment, consider, cow, damnit, darlin, darn, deaf, degree, deserve, died, enter, ew,
expecting, fall, fault, favourite, goodevening, jump, liquor, lived, mi, moments, naw, others, payment, pig, pleased, press, promise, rite, rofl,
screen, sip, spanish, sweety, tank, ten, ti, twice, using, ways [19] ai, assume, bringing, buffet, butter, cry, custom, dad, etc, exit, fan, father, fetch,
filled, hoping, ja, kinds, kno, margarita, meals, ne, neither, nicer, noooo, ook, orders, oyu, parents, pi, plenty, poop, pussy, rape, relax, report,
roof, sea, south, states, sucker, wellcome, woo, worst [18] accept, against, attention, barkeep, beat, become, bigger, bud, building, bunch, burn,
cereal, checks, chosen, clearly, closing, color, cooking, creepy, drop, easier, facing, faster, forgive, gorgeous, graders, ground, guest, invisible,
joint, lake, le, loser, mention, na, note, nvm, policy, professional, refil, regular, rubbish, sandwich, seafood, settle, smoke, somebody, space,
spaggetti, staring, style, superb, talked, tap, teaching, tempt, tender, tipsy, tracksuit, truth, twenty, urs, waste, yesterday, yh, yumm [17] aids,
anthing, blowjob, brilliant, caught, christ, common, complements, dancing, demand, difficult, doubt, due, eater, fake, familiar, fell, fo, fries,
graduated, hadnt, halo, handle, hay, helpful, hiding, honestly, ima, ohio, pan, past, paula, picked, pity, potatoes, refuse, research, ride, rocks,
salads, san, smiles, som, soooo, speciality, stomach, stopping, tha, thaks, thin, thnx, tits, tnx, track, uhhh, vine, wa, weight, west, wit, worse, wud
[16] ahhhhh, alive, appear, arm, art, bacon, born, british, broken, bus, ca, cashier, cat, chief, courses, dizzy, en, europe, figured, gracias, gross,
hamburger, harder, hat, havnt, heat, holland, holler, ik, kisses, loves, lueshi, mighty, minion, myspace, needing, net, nick, noise, oui, particular,
playin, pop, probs, proper, quality, random, raw, shoes, shop, shy, site, spit, splendid, story, tie, trashcan, vous, walking, weeks, wha, won [15]
attitude, au, awsome, aye, band, bien, block, bottom, bum, carolina, certain, chips, class, coast, code, cola, completely, cough, cover, dammit,
dogs, east, entire, excelent, fag, frank, freshly, giv, graduate, healthy, helps, hoe, hole, hug, ie, jsut, lead, liar, lying, mostly, mouse, nearly,
nother, offering, opened, otherwise, oy, per, picking, piepie, piss, player, por, preference, pregnant, present, reading, realize, request, retarded,
satisfaction, season, shell, sis, spagheti, spent, stack, sum, swear, tht, tooth, trip, typing, virginia, wel, welcom, whereever, whilst, woops [14]
amanda, apetit, appreciated, awful, ay, beach, ben, blonde, blow, bonjour, bowtie, breasts, brings, build, byee, cani, chatting, cheff, china, choise,
cleaning, comfy, costumer, credit, di, dutch, earth, ehm, ends, esle, explain, file, forgotten, further, hehehehe, kaila, lately, laughing, law, lives,
marina, marvelous, meatballs, mignion, mo, monkey, necterine, nigga, office, oki, onto, plea, portuguese, position, problemo, processor, pull,
rate, resist, restaraunt, restraunt, righto, rob, rough, russia, scotch, slap, slave, sou, soz, spegetti, sphagetti, sun, thermadore, tipper, ummmm,
verry, wee, younger, yw [13] alas, ale, anyhow, anythin, appetizers, asl, awhile, balancing, bil, boat, bottles, brown, chease, chessecake, currently,
decision, deposit, doll, dress, dressing, dry, escape, escort, following, goo, gotcha, grand, hahahah, heck, hellow, hottie, iced, idk, impressive,
item, keeps, knock, kool, lame, leftovers, likewise, lose, maar, mac, manage, messed, mission, mmmmmm, nactarine, natural, naughty, news,
noone, ny, oder, ol, options, originally, pain, pal, park, pas, pc, personality, phew, piep, pinot, plaese, poison, poo, pot, reccommend, refrain,
replace, restuarant, resturaunt, robbing, roll, rules, sadly, salt, scary, science, scotland, scrumptious, seating, seats, shirt, sight, sky, slamon, slut,
somehow, song, sory, soupe, steaks, stronger, sue, taht, tall, tasting, thak, tony, tower, uniform, usual, waht, wasted, wate, wen, wet, whered,
wise, woud, woul, www, yoink [12] abouts, account, acting, address, advice, ahoy, angry, apetite, ar, assure, attractive, awkward, beerb, beg,
behavior, belive, book, boys, cent, chesse, chinese, cleaned, clever, climb, club, clumsy, co, command, complain, considered, control, cum, dam,
dan, dang, death, deciding, delectable, delish, desk, doo, ello, entre, euh, everywhere, eww, feast, felt, fight, folow, francais, gal, gee, gettin,
gimmie, grew, grill, het, himself, horse, hum, immediately, include, keeping, key, known, lazy, leg, lemon, limit, lo, lolol, mcdonalds, million,
motherfucker, nights, noo, nooooo, normally, object, oky, particularly, payin, physics, pint, plait, planning, portugal, possibly, pounds, pour,
pronto, region, relationship, revoir, rly, robot, rum, sarah, saved, seemed, sent, several, shake, shitty, smoothie, snappy, soft, someday, spahgetti,
spelling, sr, stock, store, sur, tag, thans, thinks, thnks, tis, tom, tonite, training, trapped, travel, turns, united, universe, vege, walls, willing, wnat,
101
wut, yikes [11] aah, added, afterwards, aight, amount, angel, annoying, apologise, arrive, babies, bag, basically, beverages, blame, blueberry, bot,
bow, bro, buttsecks, buying, calm, charging, cheeky, cheesecakes, chest, chick, client, clue, comp, cooks, cos, costs, crab, cu, cyber, dis, docks,
duty, een, egg, ending, ere, everybody, ewww, experiment, exquisite, faith, foot, fork, ga, gotten, grow, grub, guessing, hop, hte, hullo, included,
j, jobs, jst, kay, keyboard, land, laws, lettuce, lift, lobter, lolz, maine, matt, mee, menue, missing, monsieur, mountains, movies, mum, naked,
nervous, netherlands, niet, nite, nu, oooo, parts, peoples, pic, pieces, plase, plastic, players, power, program, project, q, race, rain, realy,
recommendation, review, sa, scare, seeya, sexual, shove, shrimp, sil, similar, simply, snap, sob, software, soul, spare, specific, summer, system,
tahnk, tar, thang, theyll, tomato, unusual, upset, valley, ve, vegitable, warn, wild, yknow, yor [10] acceptable, accidently, ahve, alse, anyting,
anywere, apart, aware, awfully, bah, bank, beauty, berries, bottled, brasil, buh, cak, cali, cars, certianly, che, cheery, chris, clearing, clock,
comeing, complementary, confusing, congratulations, controls, den, didn, difference, direct, download, dream, dreams, drugs, dump, ed, ehh,
famous, fav, favorites, fellow, field, final, finding, flavor, force, francisco, freaking, fried, frozen, garden, gasp, geez, george, grown, guessed,
gurl, gym, ham, hawt, heaven, heheh, helllo, helped, hihi, hill, hint, hotel, howd, ihave, insult, ipe, ir, issue, iv, joined, juicy, jumpsuit, kept, kewl,
king, kinky, laughs, learned, legal, lied, lies, lik, limited, line, loose, loud, lousy, lueser, mains, mama, mark, mary, master, meh, meny, miles,
mw, named, neck, nicely, noir, noodles, nop, nor, offended, offense, ooops, op, option, passed, passing, pays, pelase, pepper, pepsi, pretend,
prices, profile, provide, punk, rd, reasonable, recently, regards, reminds, reservations, restrooms, retard, ridiculous, river, salada, salty, satisfied,
senior, senor, shows, skills, sounded, spaghettie, spaguetti, starve, stove, sucked, sunday, supper, support, tak, takin, tequila, thnaks, thru, trees,
tres, tryin, types, v, va, vacation, voice, walked, washington, wer, wher, whys, wierd, windows, workin, woudl, wrote, yuck, yummm [9] above,
accountant, admit, allergies, arse, ashamed, av, bananas, beans, berrypie, bf, bi, blend, bless, brain, brains, brooke, browse, calls, canadian, cats,
cell, cest, charming, cheat, cheating, chicago, crack, cross, crowded, crying, curse, czech, damage, das, data, daughter, decent, divine, dnt, doke,
elese, els, enjoyable, everthing, exciting, extremely, exuse, fab, faggot, flat, fok, forever, gday, german, git, gmail, google, government,
graduating, grass, gt, hahahahahaha, handy, heading, hmmmmm, holla, horny, hs, hurts, icecream, ich, ii, incredible, instant, interface, jeez, jeff,
jor, judge, kan, kiddin, kl, lay, lemonade, lesbian, lil, listed, lob, los, magnon, meaning, message, messy, mins, moves, murder, nada, natalie,
nectarines, nectorine, ohhhh, omfg, ops, organic, outfit, pancakes, patience, patient, paul, pe, peeing, pissed, plane, pleases, pocket, ppl, practice,
prick, prove, punch, purpose, push, racist, reserved, rice, rip, rule, salamon, salas, salomon, satisfactory, services, shellfish, shucks, skirt,
sleeping, soem, someones, someting, sos, sour, spagettie, spin, split, spoon, spring, stake, stalker, standard, starts, steve, stopped, strict, students,
suggestion, suits, sushi, swing, tastey, taxi, tells, tes, theif, thi, thingy, thnk, throat, train, uni, vampire, variety, vases, vegan, version, vest,
vintage, wanting, weirdo, whatcha, whew, wich, wind, wishes, wonderfull, ww, xx, yall, yess, yoiu, yuo [8] aaah, accidentally, advise, alabama,
ant, anythign, arms, asks, assist, attend, ave, bang, bartenders, basic, bay, beats, behave, bend, berrie, biatch, brad, brew, budget, bull, burgers,
bush, bze, cakes, carrots, cashed, certanly, charles, checked, cheer, cheeseburger, cherries, chew, chill, choosing, chrissy, circus, classic, closest,
cobbsalad, colour, connection, cookie, correctly, corse, crappy, crashed, cuppa, cutting, dark, disappointed, dokie, driver, duck, dying, eats, el,
ended, epie, et, everyday, everythings, evil, exams, fear, fed, filit, firm, forty, freedom, gaan, ge, gentleman, gots, greatly, grey, guide, hahahha,
har, hardly, heheheh, hero, hier, hilarious, hire, hook, hospital, hospitality, hotmail, iget, imma, impossible, info, ingredients, ireland, irl, jacket,
jersey, jogging, killing, laptop, lawyer, lick, lips, lke, lobser, loster, lovley, mail, major, manners, mature, melissa, memory, messing, mexico,
mhmm, michigan, migion, mike, mingnon, mint, missy, mistakes, mix, momentarily, mountain, ms, mz, nao, ni, nl, nog, noon, noooooo, nose,
nothin, objectives, okk, onion, ought, outrage, oven, oww, pease, perfection, perfectly, picture, pile, pink, pl, plans, plse, pork, prepares, properly,
proud, public, raised, rat, realise, respect, rl, role, saled, sally, scuse, seek, ser, seven, sharing, sheesh, simple, singing, slad, smack, sold, spag,
spaggeti, spagghetti, spinning, stat, stiff, stolen, strike, suprise, susan, switch, taller, team, teehee, tempting, tend, thirst, thnak, tight, tipped,
token, tongue, tonights, tries, troubles, truly, tx, ull, understandable, understood, upon, urself, vagina, wan, washed, wath, wave, website, weed,
weekends, whont, wht, wood, woohoo, worker, worried, yawn, yell, zealand [7] action, actress, advantage, alergic, als, americans, angeles,
appropriate, argh, attack, bac, baked, balance, barely, baseball, bere, beside, biggest, bits, bord, britain, buisness, byeee, cancel, candy, carefull,
cares, carried, celebrating, changes, cheak, chees, chery, chese, christine, cigarette, classy, clicked, clouds, coffeee, collect, como, considering,
corn, cries, crust, cus, daily, danke, dave, david, dc, decoration, denmark, desser, distracted, disturbing, dock, dosent, drag, drin, drinker, drove,
dull, dumbass, eachother, ear, eatin, edge, eighteen, energy, enything, error, errr, es, est, eten, evenin, excited, exist, expected, eyou, famished,
farm, fatty, fifty, filling, filthy, finger, fingers, fist, flattered, flavour, flew, flies, foo, freakin, fro, fucked, garcon, gender, general, georgia, giant,
goddamn, goodday, goody, grape, grazie, gtg, gulp, gun, gunna, habit, hablo, hahaa, hahahahah, hai, hanging, hasnt, hates, higher, hitting, hoped,
hrm, humm, hunny, ignoring, imported, inappropriate, inspector, interact, invented, isn, jealous, jenny, jessica, jo, joes, jou, junior, kansas,
kindness, kyrie, lack, lag, learning, lease, likely, loads, lobste, lololol, loss, lyk, magician, mais, mans, maria, marks, medication, meee, member,
mentioned, mhm, michael, midnight, migno, mines, mints, mixed, moar, momma, moms, mug, muy, nectarin, nee, nine, nj, noes, ocean,
offcourse, offered, outa, outrageous, pad, painful, parle, partner, peckish, pet, pets, pierre, placed, planet, plays, pleasse, plesae, plis, podium,
pray, preferably, president, pricey, pushy, rachel, rawr, realised, recommendations, recommended, record, redo, redy, remind, respond, resteraunt,
rigth, roast, safety, sake, salmo, sausage, sayin, screaming, security, servey, shane, showed, situation, size, skin, slight, slightly, sober, social,
somemore, somewhat, spageti, spaghtti, spahetti, specialities, spicy, spotted, spy, squeeze, stare, stood, stool, studies, subject, suggested, suitable,
sunny, tae, tail, talkative, tast, tat, teachers, technical, tempted, terrific, thankx, thee, til, tou, towards, trade, tray, tricks, trump, turning, twat,
typical, uit, ummmmm, uncle, undercooked, une, unfortunate, unique, urine, vegas, virgin, waited, walmart, war, wassup, wasting, wating, waves,
weak, weeee, weekend, welll, wench, weres, whether, whot, whyd, wi, wnt, yahoo, yeahh, yelling, yessir, yous, ys, yur, ze [6] aa, aaaa, abby,
abeer, absolutley, according, ack, actual, actualy, ad, adds, agent, ages, ahaha, ahahahaha, alex, alice, analyst, animal, animals, anothe, anyhting,
anywher, apoligize, arnt, arrow, artist, athletic, august, awake, awwww, bare, barossa, based, batch, bath, becky, becuase, beerr, beginning, bein,
bey, bitches, bloke, boston, brand, bright, bucket, built, byeeeee, cabernet, chardonnay, cheaper, cheapest, cheif, chesecake, chianti, chow,
chuckles, church, cleaner, clients, cn, cocktails, coffees, cognac, combination, comment, comped, completed, concept, contact, coob, cookies,
costumers, critic, damned, dandy, dank, dating, dentist, department, design, desire, developer, devil, difficulties, dig, directly, disappeared,
distance, dit, dive, doe, dolce, donald, dot, dough, dr, draft, dressed, dropping, drug, dumped, eatery, eer, eight, embarassed, emm, engaged,
engineering, enought, entirely, espanol, european, ewwww, ex, example, explore, ey, fart, fastest, featuring, feelings, fella, fielt, fifteen, financial,
finshed, fishing, flight, flirt, folks, forced, fortune, foul, frankly, fred, freezing, freshest, fruity, frying, gain, garlic, gd, gladly, glitch, gold, gr,
graag, grr, guitar, hahha, harm, haw, headed, hearing, hearty, hebben, hehehehehe, helloooo, hellp, hence, heyy, hiccup, hired, history,
homosexual, honor, hose, houses, howre, humor, hv, hve, ican, ight, ignore, ike, incase, including, inconvenience, incorrect, indiana, ine, inform,
information, insane, intelligence, issues, ist, itd, jaja, jew, jiffy, jim, joel, jokes, joy, kelly, kwl, labor, lager, lapdance, lawl, level, license, lime,
listening, lmfao, load, location, loll, lool, lover, luckily, mah, mall, maranara, mariana, maryland, mas, massive, matters, meanie, messenger,
mexican, miami, mignons, mijn, mineral, minut, mirror, misunderstanding, mmmmmmm, mon, moron, mrs, mysterious, naar, nam, native, nc,
nectar, nectartine, noe, nty, offence, og, okaz, okeydokey, opinion, orderd, orleans, orly, oz, paint, pair, parched, patronage, peek, pennsylvania,
pervert, plain, pleaes, poie, porn, portion, post, posted, previous, pro, products, pub, purchase, pure, quest, range, reality, realized, recipe, reg,
republic, requested, restart, robber, robots, ruin, runs, russian, ryt, sail, salat, sale, saturday, scene, schools, scientist, screwed, sed, seet, select,
102
serves, shabby, shack, shag, shifts, shocking, sittin, skinny, slide, slipped, slowly, snack, sniff, sod, sooooo, sopu, sorrz, sorts, spagehtti, spagetthi,
spain, spam, speech, spelled, spice, spoke, sprite, stab, staek, staying, stea, steel, stew, studied, suicide, suis, surprised, suspect, sweetcheeks,
swell, tah, tale, tally, teas, technically, tee, thanku, thansk, therefore, thick, threat, threw, thts, tim, tiny, tofu, toodles, tour, traditional, traffic,
trained, treated, turbo, tv, unable, unreasonable, ups, useless, van, vc, veggies, vegi, vet, vie, vino, visited, wah, waiters, washing, wed, weee,
whatd, whitewine, wicked, winw, wisconsin, witha, within, wo, woa, wooo, writing, yeap, yey, yoo, youa, ypu, yub, zone, zour [5] actor,
addiction, ae, affect, agen, agreed, alan, alaska, alberta, alike, alles, allows, alternative, amen, amenu, amy, anna, answered, answers, anythink,
apology, appetiser, appetizing, appologies, army, arrange, ash, assistance, atleast, atmosphere, auf, aussie, australian, automatically, avatar, avoid,
avoiding, bak, beacuse, bears, beerplease, befor, below, bent, berrz, beter, beyond, bie, bier, billed, bites, bj, blah, blank, blast, bleu, blushing,
bomb, boobies, bourbon, bowls, br, breast, brewed, brian, brick, brothers, bu, bub, buhbye, burned, burning, burnt, caffe, captain, carmen,
caroline, cashregister, caused, ce, cerry, chalkboard, characters, chase, cheasecake, chec, cheeks, cheesecak, choosen, cliff, cna, coca, cockroach,
cofe, combo, complicated, concerned, congrats, consume, contest, cooler, county, cours, couse, cracker, crunchy, cucumber, cuisine, curiosity,
custumer, daffodils, daisy, dancer, dangerous, debt, dee, deep, deeply, depressed, dident, digits, dink, doh, dontt, downloaded, draw, dring, dum,
dummy, dun, dus, dust, ea, easily, easter, eastern, ei, elses, entered, entertainment, enu, eny, equals, eu, evan, eve, exact, exchange, exellent, fail,
falling, favour, fee, finnished, fits, float, fluently, focus, fond, fooi, freshen, friday, frm, froze, fuk, funds, galss, gas, geld, generally, gf, gg,
gimmi, gnight, gnite, golden, golf, gona, goon, goood, grandma, gratitude, greece, greedy, growing, gues, guests, guns, habe, halp, harassment,
harbor, havea, haven, hellooooo, heloo, hesitate, hh, holly, homeless, hu, hump, hundred, hunger, hungery, hurting, hvae, ic, iceland, ideas,
illinois, illl, impressed, improve, india, industry, ingles, inglish, int, intend, intrest, io, irish, iz, jam, janet, jeremy, jerks, jess, johnny, josh, jur,
kicks, killer, lalala, lamb, lass, ld, leads, leftover, lekker, letting, levels, levitating, li, lines, lit, loaded, lobstar, locked, loke, loool, lotsa, louis,
lovly, mag, magazine, magical, maincourse, mango, marinar, marinera, mars, mass, match, math, max, meantime, meanwhile, meatball,
medicine, meen, memories, ment, mentally, meow, micro, mij, mis, misplaced, mistaken, modern, monday, monster, montreal, mooi, mu, muah,
muffin, mull, muscular, mustard, napa, napkin, napkins, ned, nerd, nibble, nigger, nonetheless, northern, nt, nurse, nut, nw, oak, obliged, oblivion,
offers, ohhhhh, oldest, om, ont, ooooh, ooooo, opening, orde, original, overs, pa, package, page, pathetic, pea, peas, peruse, peu, philly, picky,
piepi, plants, ple, pleace, pleasew, pleaz, plzz, points, poisoned, poisoning, powers, ppie, pplease, preferences, prego, proble, programmer,
protection, psst, pulls, puts, queen, rabies, rait, rats, recomment, redr, reds, refils, refreshing, regret, related, relationships, relief, rent, require,
responsible, restrount, righ, ringing, ripped, robbed, rolls, romantic, rose, sal, salaad, salda, sales, salid, sallad, salm, sample, saving, scenery,
scraps, section, sei, sends, senorita, sensitive, sever, shakes, shaped, sharp, shayla, shipment, sho, shotgun, shure, sidewalk, sim, singer, sirs,
sitdown, sixteen, slurp, smaller, smooth, snatch, sneaky, snow, soap, solid, somesing, somethign, sop, sor, spagehti, spaggheti, spaghette, spank,
speed, speek, spender, spill, spilled, spinach, spirit, spoken, spread, stacked, stan, standards, station, stickup, stinks, stone, stories, storm,
strawberry, stress, stripper, stud, stuf, success, sucka, suddenly, sugest, summers, sumthin, suop, supervisor, survay, survery, tabl, tacos, tafel,
talented, talker, talkin, tarte, tata, tats, taught, teaches, tennessee, thankye, themador, thirty, thisll, thks, thoug, thousand, thr, toi, toothpicks,
treating, truck, tthank, turkey, typed, typo, uncomfortable, understanding, user, varietal, ver, via, video, virtually, visa, vu, walks, wank, wanker,
warcraft, warning, washroom, watched, watre, wedding, weeeeee, weel, werever, wft, whay, whe, whee, whipped, whistles, whose, whould,
wide, wiht, winne, wondered, wonna, woot, wt, wth, wuld, wy, xxx, yeahhh, yeha, yesss, yoghurt, yoru, youi, yourre, youu, youy, yuor, zes,
zitten, zo [4] aaaaaaa, aaaah, aan, abou, acutally, adam, addy, advance, aged, agh, agin, agood, agua, ahah, ahahaha, ahhhhhhh, alert, ales,
allready, alredy, andreza, anithing, ans, ants, anythig, apologizes, appealing, appeared, appointment, appologize, apron, aright, arizona, arranged,
arrived, artificial, ashley, ashole, aside, askin, asleep, asparagus, assault, asshat, assistant, assumed, atable, atea, authorities, autumn, avec,
average, avocado, ba, babes, backwards, bai, ball, bankrupt, bargain, bars, bathrooms, bber, bck, becomes, beeing, belch, belongs, bene, beutiful,
bich, bird, birth, bistro, bitte, bitter, bitting, blackboard, blanc, bleh, blushes, bonus, borrow, bottoms, bouquet, boxer, bra, breaking, breaks,
breath, brewery, broccoli, brotha, buck, bugger, bummer, bumped, buona, busboy, bushes, cal, calamari, cam, cambridge, cancer, cannibal,
capital, caps, cards, casa, cauz, ccan, ceiling, celebrate, century, ch, chap, chasing, cheapo, cheek, chek, chesscake, chicks, chili, chillin, choke,
chump, cigarettes, clam, classes, cleared, clown, cnt, coach, cobbler, cobbs, colleagues, comme, commin, companion, compensate, complaining,
compulsive, computers, con, confusion, conta, continue, conto, coo, cop, costomer, cou, countries, counts, courtesy, cousin, covered, covers,
cowboy, cows, crabs, created, creative, crikey, crotch, cruise, culinary, cuming, daccord, dah, daisies, dashing, dated, dats, dawg, def, definetly,
definition, delays, delivery, dem, dentists, der, des, describe, designed, designer, despite, details, developers, devine, deze, diff, diffrent, direction,
disappointing, disaster, discover, disease, disgraceful, disguised, dishwasher, disturbance, docter, doenst, doesn, doller, donkey, dou, doyou,
drinken, drown, dujour, ebay, echt, effects, effort, ehhh, ein, elizabeth, employees, emty, engineer, enjoz, entertaining, entrees, enuff,
environment, epi, epic, ermm, erotic, estas, euros, eveing, eventually, everythin, everytime, evry, exception, exelent, expectations, experienced,
explains, fa, factor, fags, failed, fairly, fait, fallen, fallow, fanx, fare, farted, fattie, fd, fhanks, fi, fifth, figuring, fink, finland, firs, firt, fixing, fl,
flesh, fliet, florist, fools, fore, fraid, fre, frickin, frosty, frustrated, fully, gah, garbag, geen, gentle, gib, giggle, girlfriends, glarus, goed, golly,
goober, goodburger, gooood, gos, grabbed, gracious, grapes, graphic, graphics, grateful, greens, griled, grin, groovy, grumbles, grumpy, gte, gtfo,
gud, guten, haa, haaa, hace, hahahahahahaha, haircut, hank, happening, harry, haste, heads, heated, hed, heee, height, heineken, helloo, heyyy,
hez, highschool, hike, hip, hiring, holmes, homie, homo, hoor, horses, hott, hovering, hr, hummm, humph, hungy, hurr, hy, idiots, illegal, illusion,
imber, impatient, incident, inconvience, indulge, inspecting, insulted, interactions, intersting, introduce, involve, involved, iron, island, jai, jajaja,
jane, janette, japan, jason, jean, jen, jes, jet, jij, jimmy, jolly, joseph, journey, jr, julia, jumped, jumpy, juste, ka, kate, kayla, kentucky, keys,
kicked, kiding, knees, knowing, knowledge, knw, knwo, ko, kthxbai, kunnen, kyou, label, ladies, lagging, laid, languages, las, lawyers, leak, leau,
leaves, leavin, les, letter, licked, licks, lifes, lifting, lip, lisa, litlle, livin, lmaoo, loabster, loading, lobester, lobs, located, lodster, log, loo, losbter,
losing, lov, loving, lower, luelinks, luk, lulz, lush, lyrics, maby, maddam, madness, maintain, maker, mal, manchester, manly, market, martinis,
marvellous, masculine, masters, medical, meds, meer, melbourne, melted, meneer, mental, menucard, messin, michelle, minus, miranda,
miserable, mkay, mmh, model, moet, moi, monies, mor, motel, motherfucking, mroe, mud, multiple, mumbling, mush, musical, mwah, nacterine,
nae, naturally, nearby, necessary, nectrine, nederlands, netarine, nickname, nm, nom, nonalcoholic, nono, nonsmoking, nooooooo, notch, nou,
nyc, objects, officially, okee, okies, oks, olds, operation, ora, orkin, os, oups, ours, par, parent, parler, parmesan, parting, peach, pen, pencil,
penny, perv, peter, petit, phil, physical, physically, pice, pickles, piggy, pine, piping, pis, pixels, plaease, planned, pleasent, po, poisonous,
positive, powerful, prayers, pre, precious, prefers, prehaps, pretending, print, privacy, private, probleme, profession, professor, promotion,
prompt, proof, property, propose, prostitute, protect, protein, ps, psychic, puta, pyramid, quarter, qui, quitting, ranch, raping, rapist, reall, recall,
reckon, redwhine, reflect, refund, registers, rekening, rember, remeber, remote, reported, required, res, resaurant, resent, reveal, riddance, rien,
righty, rnt, robs, rong, roses, rub, ruined, ryan, safely, sah, salary, salem, salmons, salom, sarcastic, sashimi, satan, satisfying, sauteed, schnell,
screwdriver, sd, search, servers, sh, shaken, shi, ship, shiraz, shithead, showing, shutup, silent, silver, silverware, siting, sits, sixty, slices, slip,
slower, slurping, smelly, smiling, smith, snail, snake, snakes, sneakers, sneeze, soccer, socks, soep, solved, sono, sooner, sore, sorrow, sorryy,
southern, souvenir, sp, spagethi, spagettii, spaghetii, spaghettii, species, spectacular, spghetti, sphaghetti, spose, sta, stacking, stale, stars, starved,
starvin, stated, sticks, sticky, strangely, stranger, streets, strive, substitute, sucking, supose, surgery, surveys, sweden, switched, sworn, tabel,
103
tahnks, tangerine, tanked, tard, tastier, tavern, tears, teat, teenager, tellin, tere, terets, term, tex, texture, thanky, themselves, thermodor, theyd,
thqnks, thus, tic, tick, tidy, tiger, tipping, toasted, toch, tok, tolerate, tomatoes, tommorow, tooo, tourist, toy, tr, travelled, treatment, trek, tt, ttyl,
tuesday, tummy, tut, twas, twin, unacceptable, uncalled, underneath, unfortunatly, unhappy, upgrade, useful, varietals, vedgetable, veggy, veiw,
violent, waitor, waitresss, wake, wang, warmer, watere, watever, watter, waz, weeeee, weeeeeee, weeeeeeee, weer, weg, weirdest, western,
whaddya, whant, whater, wheeeee, wherver, whie, whip, wiedersehen, wilt, winston, wipe, witch, wite, wold, wolverhampton, wooooo,
woooooo, worlds, wots, xp, yas, yeas, yessss, yopu, youe, youself, yoy, ypur, yrs, yuk, yummi, yummmm, z, zeg, zero, zombie [3] aaa, aaaaa,
aaaaah, aaahhh, aahh, abit, absolutly, absynth, abuse, abut, accen, accepted, achoo, acid, actully, adams, additional, admiring, adorable, adress,
adult, adventure, afk, africa, african, afterall, afterward, ain, airport, aj, albert, alcholic, alittle, alley, allora, aloud, alrigh, alrite, alstublieft, alt,
altro, amazingly, amber, ambiance, amd, ame, among, amsterdam, anal, ancestors, andrea, andsome, anice, ankle, anns, anoter, anothr, anser,
antoher, antonio, anus, aol, apartment, apetizer, appearance, appetising, apples, appliances, appreciation, appricated, aqui, areyou, argentina,
aroma, arrested, arrows, arts, asap, aseat, assigned, associate, assuming, asswipe, atm, aub, aunts, austin, avez, awater, aways, awwwww,
babysitting, background, badly, bagel, bambi, banker, barbie, basement, basil, basketball, bb, bbeer, bc, beast, beatiful, beating, beautifull,
became, beee, beery, beet, begging, behalf, behaviour, beleive, belly, belong, benny, bery, beryy, betcha, betta, betty, beware, bibi, bientot,
biggie, bike, billl, bing, biology, biotch, bizarre, bk, blackett, bleed, bleeding, blew, blink, blocking, blond, blouse, blown, blows, bneer, bo,
boats, bod, boi, boiled, bol, bone, boob, booked, books, booth, booty, booz, boredom, bossy, bothering, bounce, boundaries, boxes, brave,
brazilian, breakfest, breaky, breat, breathe, britney, brittany, bubba, bueno, buke, bun, burns, burst, busier, busted, buster, butcher, buttsex,
buuuurp, camera, cancun, cannibalism, cap, caring, carpet, catching, caucasian, causes, causing, cd, celery, center, central, ces, cha, chaching,
chalk, challenge, champaigne, champion, changing, charity, charlotte, charmer, chatbot, chateu, cheack, cheddar, chedder, cheering, cheerio,
cheesesake, chekc, cheques, cheryl, chesee, chickens, chiken, chile, choked, chomp, christian, christina, chuckle, chug, cindy, civil, cka, ckeck,
clicking, closet, cluttered, cocaine, cocksucker, codd, cofffee, collection, colored, colt, comeback, comfort, comida, communist, companies,
complaints, complet, concern, condition, conlan, considerate, construction, consumed, contain, contains, convenient, conversations, coolio, coool,
copper, copperfield, corked, cosa, costumes, coul, countryside, coup, cousins, coz, crash, crashes, craving, crawl, crayons, creamy, create,
creating, creator, credits, crisis, crockery, croutons, crunk, cuenta, culture, cure, current, cursed, cutlery, daddy, dads, dag, dal, dale, dallas,
dangit, danku, dar, daydreaming, daylight, ddrink, deadly, dears, deathly, debil, decaf, december, decor, deer, delaware, delcious, delicacy,
delicius, delights, delivered, denver, dere, derick, dersert, designers, develop, device, dey, dial, dicks, didt, diego, differnt, digital, ding, dint,
diploma, disappear, discipline, discovered, discuss, disguise, disorder, ditch, ditto, divorced, dobry, doggie, doggy, dollface, dolls, donde, donna,
donw, doomed, doors, dotn, doup, dour, dove, dow, drain, drawn, dred, drik, drill, drives, drops, drums, duel, eager, earl, ears, eated, ebeer,
economics, ee, eek, eheh, ehehe, electronics, embarassing, emnu, emo, employee, energetic, engine, englisch, ensure, eraan, escargot, espresso,
eta, eternal, etre, euhm, evertyhing, everyones, evilavatar, ewwwww, exam, exceptional, exclusive, excuses, exists, exited, exotic, expansive,
expert, explicitly, exploded, expresso, eyebrows, eyup, faces, facture, failure, fala, fame, familar, families, fantabulous, fantasies, fap, farther,
fatass, fate, fatten, features, feelin, fer, fiance, fiber, fiesta, fiesty, fighting, filets, fille, filler, fillets, fills, filt, finance, finds, fineshed, finishing,
fishy, fixed, flattering, flattery, flippin, flipping, floats, flow, flowwers, flustered, forks, fot, fourth, fox, francaise, freeze, frustrating, fu, fuckface,
funeral, funky, fur, fuzz, fuzzy, gang, garnish, gather, gbye, gedaan, geek, generic, geography, geta, getcha, geven, ggood, ghost, gie, gimlet, gip,
girly, glares, glue, goat, godd, gods, godspeed, goig, gon, goodnite, goods, goof, goofy, gordon, gourmet, grad, grandmother, grat, gratis, greasy,
greeting, groups, grrr, grrrrrr, guiness, gusta, haev, hahaaa, hahaaaa, hahaah, hahahaah, haldo, halt, hamburgers, hammered, handed, hannah,
hansome, happend, hardcore, harvard, hassle, hated, havr, hawaii, hawk, hax, hazard, headache, heavens, heb, heey, hehee, heights, held, hellooo,
hellooooooo, helmet, herself, heylo, hha, hick, highway, hii, hiii, hiiii, hing, hink, hmn, hobo, hoi, holiday, hom, homemade, honour, hoo, horde,
host, hottt, hou, houise, hous, housewives, hoy, hoz, hre, hthat, htis, hugo, humans, hungary, hungrey, hunt, hunting, ia, ici, icq, idd, identity,
ididnt, iep, iepie, iets, iis, ile, image, imagination, immediatly, impress, ina, inches, includes, inconvinience, incredibly, indoors, inedible, infact,
informed, infront, ingredient, inhaled, init, innocent, inspection, installation, instructor, insulting, insurance, intelligent, intended, interaction,
interactive, interests, international, internets, interview, investment, invite, ip, ironic, ish, itself, ittt, iwill, iwould, iyou, jak, jazz, jeeves, jerkoff,
jewels, jj, joey, jog, joker, jonathan, jones, joure, ju, juggle, juggling, june, kanpai, karate, keg, kein, keine, ketchup, kingdom, kissing, kitcken,
kitten, kiwi, knee, knocked, kom, korean, kosher, krijg, kung, kus, labster, lad, laddition, laggy, lala, landscapes, lard, larry, laters, latest, latte,
latter, laura, lesson, lest, letters, levitation, lighter, likee, likw, lily, limits, limp, link, liquid, liquids, liverpool, lll, lmaooo, lobby, lobseter, lobst,
locally, lock, loks, lolo, lone, looka, loosen, lour, lovelly, luke, luv, lv, macaroni, macs, mafia, magnificent, magnum, magon, mai, maiden,
mainly, maison, makin, managment, map, maranaria, march, marie, massachusetts, massage, matches, matey, mayb, mayo, mc, meaningless,
med, meeeee, mei, meme, menuuuu, mere, metta, meu, mid, mignom, mignot, milady, mildly, military, milkshake, miller, millionaire, minit,
minogn, minor, mio, misses, misspelled, mmhmm, mmkay, mn, mode, models, mofo, momen, momento, mone, monica, moo, moon, mornay,
mot, motherland, msut, muc, muffins, mugged, munch, muppet, murdered, muscle, muscles, museum, mushroom, musician, mute, muther, nacht,
nahh, nap, nas, nate, national, nay, nebraska, necartine, necrarine, nectarinetart, negative, neighborhood, neva, nevar, newspaper, niece, nin,
nineteen, ninja, nipples, noble, nods, noice, noisy, nooooooooo, noooooooooo, notify, nova, november, nowadays, noz, nutty, oaky, obey,
observe, obvious, oc, occasionally, october, odnt, oe, oeps, ofcouse, offend, offensive, ohhhhhh, ooohhh, oooohhh, oooooh, oop, opium, opposite,
oral, oranges, ord, organization, orgasm, othe, ould, ourselves, outragous, outstanding, ovens, overseas, owned, owns, oxford, pacific, painting,
palace, palce, paris, particulary, partying, passes, passion, pastry, payd, paz, peaceful, peaches, peanut, peanuts, peekaboo, peie, percent,
perchance, period, permission, persons, pete, petes, peut, phd, philadelphia, philippines, philosophy, picks, pics, pictures, pier, pills, pimp,
pineapple, pirates, pisses, pit, platter, pleasw, pleaze, plez, pliz, pointless, pointy, political, pool, popped, pops, poptarts, porfavor, portions, posh,
potato, pots, pound, preferred, prefrence, pregnancy, premises, pressing, pressure, presume, prettier, pride, primary, prison, probly, produce,
produced, product, proposal, protest, psh, psych, psychiatrist, psycho, psychology, pue, puke, pulled, pulling, pun, purchases, purse, quebec, quer,
questionaire, questioning, quicker, quiz, rabbit, racial, rack, rad, raining, raising, ram, randomly, rank, raped, raven, ray, rdy, reaching, rear,
reasons, reccomendation, receipt, receive, recent, reciept, recieve, recognize, recommed, recorded, redheads, redred, reduced, reed, refresh,
refused, registr, relatively, released, religion, rely, remain, reminder, remodeling, rendered, rep, replaced, replacement, reply, response, restuarnt,
result, retrieve, riding, rings, roles, rom, romania, root, rotfl, row, royal, rpg, rudeness, runner, runnin, rustle, sabrina, sacrifice, sala, salard,
salmom, salsa, salted, samantha, sammich, sandra, sandwhich, sandwiches, santa, saucepan, saucy, saumon, scaring, score, scotia, scotty, scream,
seak, sean, secon, secretive, secrets, seizure, selected, selena, selfservice, semester, sentences, seperate, servant, setak, seu, severe, sexist,
shaghetti, shaking, shant, shirts, shithole, shock, shoo, shopping, shorter, shoud, shoulda, shoulder, shouting, sin, singapore, sins, sirt, sisters,
skeet, skill, slack, sleepy, slim, slit, sloshed, sn, snuck, sof, softly, solo, somehing, songs, soooooo, sophomore, sori, soryr, sould, sow, soy, spa,
spagati, spagatti, spagattie, spagette, spaggethi, spaghett, spaghettin, spaghettis, spaghetto, spaghitti, spanking, sparkling, sparta, speacials,
speaker, specail, specails, specifically, speedy, spegitti, speicals, spel, spero, spiffy, splash, spoons, sports, sporty, spoup, stage, stalk, stalking,
statements, stays, steake, stealin, stek, stella, stephen, steven, stfu, sticking, stiffed, stiller, stingy, stinky, stoppit, strangest, strech, strength, strip,
stroll, struggling, stumbled, stunning, sub, subjects, sucky, sugartits, suggesting, suggestions, suk, supe, supporting, suprised, surf, surgeon,
104
surprising, sux, svp, swearing, sweaty, sweetest, sweets, swimming, swore, sydney, syndrome, syrah, systems, taart, taco, tad, tada, talent, tape,
tavolo, tax, tear, tech, telephone, telll, tends, tense, testicles, testing, thailand, thamks, thankies, thankss, thankz, thanls, thant, thatnk, thax, thea,
theory, thier, thingie, thirteen, thnkyou, thoughs, thougt, thre, threesome, throws, ths, thsi, thursty, tink, tire, tlol, toda, toes, toilets, toke, tone,
tons, tont, tonts, tossed, tout, tow, tragic, tranny, traveling, treasure, triple, tripped, tryna, ts, tuck, tune, twelve, twilight, twinkle, twisted,
twitching, tyeah, ud, ughhh, uhhhh, uma, umbrella, una, unaware, underage, underwear, ungrateful, uniforms, unlimited, uo, urinate, url, urm,
urp, ut, uve, uz, vai, vanilla, vanished, veal, veer, vegeatble, vegetarians, vegie, venezuela, verz, village, vin, vinnie, virus, vita, vol, volgens,
vomit, voor, voulez, voyage, waar, waffles, wages, wai, waist, waitre, waitres, waitressed, walke, walker, warmed, warned, wather, watiress,
watr, wauw, wc, wealthy, web, websites, weclome, wednesday, wein, welcoem, wenst, werid, werre, wery, whack, whar, whatre, wheat,
wheeeeeee, whet, whines, whites, whoah, whold, whoop, wid, wie, wierdo, wii, wijn, wilmington, wime, winer, winks, winr, winter, wir, withe,
wiv, wolf, wolves, wone, woods, woooo, workout, worrys, wou, wouldn, wouldyou, wounds, wpuld, written, wrk, ws, wter, wts, wuh, wunt,
xbox, xcuse, yaa, yar, yeaa, yeaah, yee, yelled, yesh, yno, yoohoo, yorkshire, yougo, youlike, youm, yourwelcome, ythank, yucky, yupp, yuppp,
yyes, yyou, zeah, zet, zijn [2] aaaaaaaaaa, aaaand, aare, abd, abilities, ability, abomination, abot, abotu, abour, abroad, abstract, abt, acapulco,
access, accidental, accomodate, accomodating, accompany, accomplished, accountants, accounting, accurate, achtung, acount, acrobat, activities,
activity, acts, acually, acum, addicted, addicting, addictive, adding, addition, adesso, adieu, administration, admire, adrink, advances, advertising,
afara, affecting, affor, afterdinner, afters, ag, againg, agile, agrilled, ahahah, ahahha, ahha, ahhhhhhhhh, ahm, aid, aieee, aiming, airplane, ak,
aked, akward, ala, alaskan, alcahol, alchoholic, alcoholics, alcool, algorithm, alien, alison, alla, allergy, alligator, allision, allo, allowing, allrighty,
ally, almighty, almonds, aloha, alreadz, alryt, alter, altogether, ambience, amiga, aminute, amnesia, amuse, amused, amusing, anbd, anda, anders,
andrew, andyou, anger, angle, angus, anhow, animation, ann, annie, annoy, ano, anos, anoying, answering, anticipation, antsy, antwerp, anyfing,
anyrhing, anythime, anythng, anywheres, anywho, anz, ao, aoup, aout, apatete, apatizer, ape, aperitif, aplogies, apointment, appitite, appitizers,
application, apply, appologise, architect, architecture, areas, argument, ariel, arn, aroun, arr, arre, arrivederci, arround, ars, arsehole, arsenic,
artistic, asi, assface, asthma, astronaut, athe, atl, atlanta, atlantic, atrocious, attached, attempt, attimo, auch, aunt, aurevoir, aus, aussi, austria, aut,
authentic, author, auto, autograph, aux, avatars, awa, awaken, awaz, aweosme, awesomeness, awfull, awhole, az, bab, bacardi, backs, badass,
bakersfield, bakewell, balanced, ballerina, balloon, bandaid, banging, banna, banned, barf, bark, barkeeper, barmans, barmen, barn, barrier, barry,
base, basis, basura, bbe, bbq, bbye, bday, bea, bead, beam, beaten, beaucoup, beaux, beb, bebe, becoming, beeeeeeer, beeeer, beeerb, beep,
beerbeer, beerd, beert, bees, beforehand, begining, begone, behindthe, beijing, belgian, believer, bella, belle, bello, bem, bending, bends, beneath,
benissimo, bern, berri, betalen, bethe, betsy, bettys, bible, bid, biertje, bij, bikini, billet, billing, billion, billy, binge, bint, bio, biochemistry, birds,
birmingham, biscuit, biscuits, bisogno, bisque, biter, bladder, bland, blanket, blargh, bleach, blech, blending, blessed, blessing, blijven, blimey,
bll, bloated, blocks, bloem, bloemen, blog, blondes, blondie, blowjobs, blueberries, blunt, blurry, boards, bobs, bodies, boger, bold, bollocks,
bond, bonne, boof, boogie, boost, bootle, booye, bordeaux, bore, borrowing, bosom, bothers, botle, boutique, bouts, bows, bowties, boxers,
boyardee, boyfreind, boyfriends, brady, braised, brash, brasileiro, brasilian, bravo, brazilians, breakdance, brewski, bribe, bricks, bridge, brighton,
brit, broad, brokd, brooklyn, browneyes, bruce, brunello, bruno, bto, bubi, buddies, buddz, bude, buenos, buggy, bugs, buisy, buiten, bullshit,
bump, bunches, bunny, buns, buon, burbon, burped, busines, bussed, bussiness, bust, butterworth, buttons, buuurp, bwa, bwah, byeeee, byer,
byte, bywe, cabbage, cable, cach, caesar, cakce, calculate, calgary, calif, calories, camel, canaria, canhelp, canibal, cans, cantonese, canu, capisco,
carbonara, cardboard, cardiff, career, carefully, carlita, carly, carne, carol, carrot, carrying, cashing, casino, cassie, castle, castles, cataracts,
catholic, caunt, cavern, cech, celebrity, certified, certinally, cervesa, cf, chack, challenging, chamange, champ, champaign, champain, champane,
champayne, champions, channel, chantelle, char, chardonay, charges, charlie, charm, chased, chaser, chateau, chattin, chatty, cheaks, cheapass,
cheapsake, chears, checke, checkout, checkplease, checkpoint, cheecake, cheeers, cheep, cheerios, cheerleading, cheeseake, cheeseca,
cheeseckae, cheesescake, cheesesteak, cheeskake, cheeze, cheffy, cher, cherie, cherr, cherrios, cherrry, cheseecake, chewing, chewy, chheers, chi,
chien, childhood, childish, childrens, chilis, chipped, chk, choking, cholesterol, chooo, chopping, choppy, chops, chowder, christmas, chrisyu,
chronic, chuck, chunk, churches, ci, cibo, cinema, cipriani, circles, cities, clarify, claude, claw, clawed, clears, cliente, clik, climbed, clinic,
clinton, closely, closes, cloth, clothing, cloud, clubs, clutz, cobbc, coctail, codeword, coem, coff, coffeeee, coffiee, coiming, colada, colder, coli,
colleague, collecting, colleges, collegues, colon, colorado, combine, comedian, comer, comic, comments, communication, community,
companionship, compare, compared, compensation, competent, compleat, complement, completly, complex, compliementary, complimetary,
compost, compsci, compte, compute, conditions, confidence, confortable, confuse, connais, connected, connections, connor, considerably,
consumption, cont, contents, continued, controlling, convenience, convention, convince, convinced, convo, coofe, cooker, coolest, cooling, coop,
cooperate, coordination, coors, cope, copy, corners, corrected, cosi, coughs, counted, counters, court, coy, cpa, cpr, cr, crackalacking, crackers,
crawling, craziest, creatively, creators, creep, creeped, creeping, crisp, crook, crossing, crouton, cruel, crummy, crunching, crushed, crusty, cruz,
crystal, cuanto, cud, cuisinart, culo, cultural, cumin, cumslut, curses, cuss, cust, customar, customors, cutiepie, cuts, cuzz, cycle, dacht, dairy, daj,
dakujem, dalla, damit, dammi, dancers, dances, dandelion, danielle, dared, darned, ddint, deadbeat, dealer, dealing, dearest, debating, debbie,
debts, decidous, decieving, decisions, declare, decline, decorations, dedicated, deeper, dees, defeat, defying, degrees, dehydrated, deja, dejour,
del, delecious, delete, delicios, delight, delte, demanding, denk, dente, dentistry, depart, dept, derby, deser, deserv, deserved, desery, deset,
desires, desktop, desrt, desssert, destroyed, deutsch, developed, development, dew, df, dickens, didnot, didu, dieing, dies, dieting, differant,
differently, differnet, digest, digestion, digg, dikes, dilicious, dinners, dinning, dire, dirt, disability, disagree, disappearing, disclaimer,
discombobulated, disconnected, discounts, discussing, disgrace, dished, disposed, disrespectful, distinctive, district, distro, djour, dno, doctors,
dod, dodgy, doen, dogg, doi, dokay, doki, doky, dollors, domestic, donate, doning, donne, dony, doobie, doorway, dor, dork, dorsia, dose, dosnt,
douche, douchebag, dovi, dowqn, dozen, dp, dpc, drab, drama, dramatic, drats, drawing, dreadfully, dressin, dri, drinke, drinkl, druck, drunken,
drunnk, druuuunk, dud, dujor, duke, dumbells, dumbo, dumps, duno, duper, dustbin, dusty, duuuude, dw, dya, dynamite, eagle, earlyer, eascape,
ease, eateries, eau, eccentric, ecco, economy, ecuador, ecuse, eddie, edges, ednet, eeer, eeh, eerb, eet, effect, efficient, egregious, egypt, eheheh,
ehmm, ehrm, eine, eipe, elaborate, elas, elase, elce, elder, eleven, ell, ella, elle, elope, elvis, emails, embarrassed, emergency, emilion, emily,
emotional, enchanting, encounter, eney, enforcing, engagement, englishman, enjoyment, enlighten, enoug, entertain, entertained, entry, enuf,
environmental, enyoy, enysing, episode, equal, erd, eres, eric, erik, erin, erious, ermmm, errmm, escaped, ese, espana, essex, esta, establishments,
este, eternity, etes, ether, eugenio, eugh, euro, ev, evaluation, eveinig, evenings, everithing, evidence, evildude, evning, examine, examines,
excellant, excused, exept, exhausted, expanding, explode, exploding, exploring, expression, exscuse, extraordinary, extras, exwife, eyed,
facilities, factory, faithful, falls, fancypants, fantas, fantastico, fapfapfapfap, farmed, farts, fatso, fatter, fave, fdp, feces, federal, feedback, feeds,
feee, feeel, feisty, felit, felling, females, fence, feng, fernanda, ferrari, fields, fiet, figaro, figures, filed, filer, filete, filiet, fillat, fillin, filtet, filwt,
fina, finals, fing, fini, finishd, finito, finsh, firts, fisherman, fishermen, fitness, flair, flakes, flash, flee, fleet, fleigh, flet, flip, flirting, floors, floral,
flour, flowes, flown, flu, fluent, flwoers, fock, folo, foood, fooood, foooood, football, fora, foreigners, foreplay, forest, forgetful, forgiven,
forgoten, forklift, formular, forr, fortified, fortytwo, forum, fourteen, fowl, fr, fransisco, fraud, freebie, freebies, freeeee, freezer, freind, frend,
fresher, frig, frige, friggin, frighten, frightfully, frinds, fringe, frink, frisbee, frisky, friut, frosted, fruti, fry, ft, fucktard, fuker, fulfilling,
fullscreen, function, fungus, funhouse, funk, funner, funy, gae, gaem, gag, gald, gam, gambling, gamefaqs, gamer, gaming, gangster, ganna,
105
gardener, gassy, gauti, geeft, geesh, genius, genre, gente, gently, gess, getin, geting, getta, getti, gewoon, gfd, gh, ghetti, ghetto, gig, giggles,
gilled, gimp, ginger, ginny, giornata, givin, glaasje, glade, glasse, glasss, glassy, glaze, glory, gloves, glss, glued, gna, goal, gobble, goggles,
goign, gonan, gonig, goodafternoon, goodbuy, goodlooking, gooey, gooo, gooooood, goooooood, goose, gor, gorgous, gorjeta, gota, goto, gp,
grabs, grades, graduatin, grammar, gran, grandfather, grandmothers, gravity, greatday, greate, greatest, greets, grief, grigio, grille, grinding, grins,
gris, groans, groundhog, grrrr, grrrrr, grunting, guessin, guinness, gulps, gum, gums, gustavo, gut, guts, gwenna, haaha, hab, habits, hablas, hable,
hacve, hagha, hahahaaaa, hahahahahah, hahahahahahahahaha, hahahahahha, hahahahha, hahahhaha, hahahhahaha, hahhah, hail, haired, hairstyle,
hairy, halfeaten, hallooo, hammer, han, handicapped, handicaps, handjob, hangin, hangover, hanks, happily, hardest, harhar, harrassment, hash,
hasta, haunted, haus, hava, haves, hayfever, hd, hea, headin, headless, healthier, heare, hears, heather, hectic, hector, hedgehog, heeft, heel, heer,
heheheheeh, hehehhe, hehhe, hel, helll, hellllllooooo, hellllooooo, hellloooo, helllooooo, hellloooooo, hells, helooooo, hem, hen, henry, hense,
herbs, herd, heree, herer, hermit, heroin, herpes, heure, heve, hgave, hhah, hhaha, hhahaha, hhehe, hhi, hhmm, hhow, hid, hiiiiii, hik, hiking,
hilary, hilly, hits, hittin, hlo, hobbies, hobby, holdin, holidays, hollywood, homer, homework, homey, honeymoon, hong, honk, honored, hooch,
hooked, hooker, hoop, hooray, hooters, hopes, hops, hor, horizon, horribly, horrid, hotstuff, hotter, houston, howold, howzat, hqve, hrmm, ht,
htat, http, huckleberry, hugs, huhu, hummer, humour, hung, hunh, huni, hunk, hunter, hunters, hurl, hurrah, hurricane, husbands, hussy, hustle,
hw, hwat, hwllo, hye, hyper, ian, icant, icarus, icon, idc, ideia, idol, idont, iesi, ig, ignorant, ignores, iii, iim, ij, ijust, illhave, illiterate, ilove, ily,
imaginary, imm, immature, imperial, impersonator, imply, importantly, impression, inch, inclined, incompetent, inconveniences, incovenience,
indeedy, indian, indigestion, industrial, ineed, informing, injuries, inner, innocently, innuendo, ino, inquiring, ins, insanely, insisted, inspectors,
institute, intense, intensive, interim, interior, internal, interpret, interrogations, interrupting, interst, intrigued, intriguing, intuition, inventive,
iowa, iq, ironically, isee, islands, ismy, isso, italiano, itch, itss, iu, iwll, iyes, iyt, jackass, jacked, jackson, jacksonville, jacques, jail, jaket, jako,
jamie, january, japanese, jar, jasmine, jawesome, jax, jay, jays, jebus, jeden, jeej, jeepers, jeeze, jeffrey, jelly, jenni, jennie, jep, jer, jewish, jif,
jimbob, jin, jocking, johnson, jon, jordan, jose, josephine, jours, jouw, jsi, juan, juicer, july, jumping, juor, jup, jure, juss, justice, jw, kalika,
kassa, katie, katy, keen, keepem, keisha, kekekekeke, kerri, keyboards, kg, kia, kiki, kisha, kissed, kitties, kkk, kkt, klaar, klant, kleptomaniac,
km, knickers, knoe, knowwhat, kobe, koi, koke, komt, kona, korea, kosten, kow, ks, kvety, kyoober, kytky, laat, labas, labels, lachen, ladder, lads,
lagers, lags, lah, lahe, lalalala, lalalalala, lambrusco, lamp, lar, larger, laten, latin, latino, laughed, laundry, laut, lava, laying, lbster, leche, leek,
legend, lei, leisure, lend, ler, letme, leve, lez, lhave, licour, lieing, lier, lighten, lighting, lightning, lights, likea, limonade, linda, linguini, linkin,
lipstick, liquidy, lisbon, lite, liter, literally, liver, llamo, ller, llobster, lobsterlobster, loco, logged, logging, logic, logs, loike, lok, lolll, lololololol,
lolololololol, lolyou, lombster, loneliness, lonley, lookat, lookit, lookout, loop, loosing, lordaeron, lorfma, lotta, lottery, lou, loudly, louise, lovee,
lovel, lovelz, loverly, lovingly, lowly, lpease, lu, lubster, lucy, lump, lust, luvvy, lz, maan, mabye, macdonalds, mace, machines, madaam,
madamme, mademoiselle, magarita, magnetron, magnifique, makeing, maken, malt, mamma, mamn, managed, managers, mandarin, mandi,
mangiare, manhattan, manicure, manner, manu, marathon, margerita, marianara, maried, marihuana, marinari, marinata, mario, marked,
marmalade, marninara, marty, marys, mast, matched, maudit, maury, maybey, mccartney, mdear, mea, meatloaf, meditate, mediumrare, meeee,
meetin, meine, melons, members, menacular, meni, menion, meno, mens, mer, merry, meself, messages, messanger, meta, meter, meth, metro,
mgmt, mhave, mhmmm, mian, mice, mich, michelin, mick, microware, microwaves, midlands, migh, migron, mile, millionth, milon, minded,
minds, minerally, mini, minimal, minimum, mino, minors, mintues, minues, minuts, mirana, mirrors, missions, missouri, missus, mixer, mmmh,
mmmhhh, mmmmmmmm, mnam, mname, mnu, mobile, mocha, mock, moeten, mogan, mognon, mojito, molest, molesting, molto, moneys,
mongoose, monsiuer, mony, mooie, moonlight, moooore, moove, morels, moreso, mosaic, motherfuckin, moustache, movements, mozes, mt,
muhahahaha, muhahahahaha, muito, mumble, mums, munchies, mustnt, mustve, mutt, mutter, mutters, mwaha, mwahaha, mwahahaha, myslef,
naah, nachos, naco, nai, nailed, nails, nametag, napis, nathan, nation, nature, natuurlijk, nayhting, nearer, nearest, neather, necatarine, necatrine,
neccessary, neco, nect, nedd, nederlanders, neer, neighbor, neighbourhood, nein, neisha, nej, nerds, nerves, nervously, neverland, nevim, newb,
newport, newscientist, nic, niceeee, nich, nicotine, nie, niente, niether, nifty, niggas, nigh, nightmares, nignon, niiiii, nile, nip, nish, nit, nive, nix,
nobodys, noce, nod, noi, noises, nonono, nonsense, nookie, noones, noooooooo, normaly, norway, nous, nout, nowwwwww, ns, nuclear, nude,
nudge, num, numb, numbers, numerous, nurses, nursing, nutricious, nuttin, obrigado, occured, oclock, ocupation, od, oddly, ofen, offerto, ofzo,
ohhhhhhh, ohi, ohk, ohw, ohyeah, oj, oka, okidoki, okkk, okm, okok, okydoky, ole, oliver, omdat, ome, ontario, onze, ood, oof, oohhh, ookay,
oooohhhh, ooooooo, oooooooo, ooooooooo, oopsie, openings, operate, opie, opportunities, optional, oreder, oregon, orer, organize, ornaments,
orrible, osrry, othet, ough, oughta, outback, outdoors, outlandish, overcooked, overflow, overhere, overlook, overly, overthere, overworked,
owed, owh, owuld, owww, oyyyy, pace, pack, pagare, pages, pai, painted, paintings, pairs, pale, panda, pane, panties, para, parden, pardus, pariu,
parked, parles, parlez, parrot, partake, partay, partially, partly, passenger, pastas, paste, pastime, pate, path, patio, patron, payments, payyy, peak,
pecan, peculiar, pedo, peed, peepee, peice, peppered, perfer, perfetto, perform, performance, perfume, perha, perhap, perrier, peru, perve, pest,
peta, petty, peux, pey, pff, pffff, pfft, phat, phobia, phrase, piano, pica, picket, picnic, piee, pieee, pigs, pilaf, pill, pilot, pils, pina, pino, pints,
pioe, pipe, pir, pirate, pire, pissing, pitch, pitt, piu, piue, pivo, pixelated, plaats, placard, placement, platinum, pleaase, pleaseeee, pleaseeeee,
pleaser, pleash, pleasr, pleasre, pleasurable, pleaswe, pleease, pleeeease, pleeeeeease, pleez, pleqse, plesa, plesse, plesure, plkease, plox, plum,
plump, plwase, plx, poco, poeple, poi, pointing, poisons, pokes, pole, politics, polly, pond, pony, pooped, poorly, poptart, porridge, portal, porter,
porti, porto, portugese, portugues, posible, positively, possibility, posso, poste, posto, potatoe, potatos, potential, potter, poured, pourrais,
powdered, powered, pplz, pra, practical, practicing, prank, prasau, prattville, prease, precarious, precies, prefered, preffer, preggers, premisis,
preparation, preparing, preposterous, presence, presentation, pressed, preston, prett, prettiest, prety, prevent, previously, pricing, pricy, priest,
principle, pritty, prize, probally, probelm, problamo, processed, processing, procreate, programmed, programming, progress, promised, promote,
promoted, pronunciation, proove, proplem, protecting, provided, pry, pu, pubic, puddings, pukes, punani, punctuation, puny, purely, purple,
purty, pushing, puss, pwned, py, pyramide, pyschology, qualified, quando, quantities, queer, quel, quench, quickie, quiero, qw, races, racket, rae,
raincheck, rains, rainy, raise, ralph, ramsay, ramsey, randy, rap, rash, rasist, rating, rea, reached, realli, reallz, rebel, reccoment, receit, recognise,
recomended, recoment, recommened, recompense, recovering, redbull, rede, redi, redre, reduce, reeses, refering, referring, refilled, refreshment,
refurbishment, refusing, regard, regester, regi, regist, registar, regularly, regulars, regulations, rehab, reheat, reject, relate, relative, relatives,
relaxed, relaxing, relieve, relieved, remembered, remembering, reminded, rendering, renovated, renovating, renovation, repent, repet, reporting,
represent, reputation, reserve, reside, resolved, resourceful, respectable, respectful, responce, responding, restarant, restaruant, restauranting,
restless, restrant, restricted, restrunt, resturaunts, ret, retarted, returning, reversal, revise, revision, rew, ribs, richard, rick, ricky, riesling, riiight,
rik, ripping, risk, rit, rith, ro, roach, roasted, robert, robis, rocked, roep, roger, roleplay, romance, ronald, roomate, rooms, ros, rosso, rotten,
roughly, rowdy, rows, rr, rubber, rudy, ruining, runescape, rushed, rushing, rustig, rusty, ry, rying, saa, sacramento, sahara, saint, salald, salate,
salinas, salmen, salmin, salmonm, salutes, saman, sammiches, sandolery, sapghetti, sara, sarcasm, sars, sassy, sauv, saves, savior, sb, scale,
scelto, scenario, scent, schedule, schon, sciences, scientific, scientists, scoliosis, scored, scoth, scottish, scratch, screams, screenshots, scrummy,
scurvy, scusi, searching, seas, seasonal, seasons, seattle, sebastian, secks, secretary, secs, selfish, selling, selves, semesters, sempre, senile,
sensible, sentence, separately, september, series, seriousness, servin, session, sessions, seta, sets, settled, settles, seventy, severely, sevice, sexes,
shade, shadows, shaggy, shakin, shal, shape, shared, shaved, sheep, shelter, shezams, shine, shipped, shipping, shitfaced, shitting, shoe, shoop,
106
shops, shorts, shorty, shots, shoulders, shower, shown, shrimps, shrug, shud, shui, shuld, shuttle, siblings, siddown, sides, sideways, sier, sighs,
signature, sike, silk, simbaa, sirm, sitr, situations, siy, skank, skanky, skater, ski, skillet, skint, skirts, skwared, slag, slaps, slaves, slick, slightest,
sling, slips, slobster, sloppy, slovakia, slur, slurpy, slurred, smakelijk, smaken, smash, sme, smiths, smog, smokeing, smoker, smokin, smooch,
smothered, smth, sneak, snel, snooty, sobremesa, sobs, sociology, sock, sodas, sodomy, soe, sok, solve, somehting, somenthing, someplace,
somethingto, somethng, sometim, somsing, somtimes, somting, sons, soop, soory, sooup, sophie, sopup, sorbet, sorr, sorri, sorrows, sorrryyy,
sorrym, sorta, soryy, souo, soupd, source, southwest, soveneir, soviet, sowhat, sox, spagehtii, spagetii, spagheeti, spaghettti, spaghtii, spaghuetti,
spaguetthi, sparkle, sparks, sparse, spartaaaaaaaaaaaaaaaaaaaaaaaaa, spat, speach, speack, speaks, specialist, specially, specialties, speechless,
spegatti, spegeti, speggetti, speials, speller, spending, spi, spices, spider, spills, spirits, spoil, spongbob, spooky, sport, spots, spreek, sprint,
spritzer, squared, sreak, staat, stable, stabs, staffs, stai, stairway, standin, stands, stanislaus, stds, ste, steaking, steamed, stearing, steep, steka,
stepped, sth, sthat, stink, stinker, stir, sto, stol, stong, stoop, stoppin, stops, storie, stormin, storms, stp, straightened, straks, strangers, straw,
strawberries, streak, stressful, strictly, striking, stripe, strippers, strips, stroke, studing, stuffs, stuped, stupidity, stupied, stylist, sublime,
successful, succulent, suffice, sufficient, sugary, suger, suivez, sul, summet, sums, sumthing, suns, sunshine, suntory, suo, superman,
supernatural, supersized, supports, supposedly, suree, surpr, surprisingly, surreal, surrounded, suzi, sw, swamped, swap, swd, sweat, swedes,
sweeeeet, sweeet, sweetness, swim, swine, switzerland, syour, tabe, tadaaaa, tady, taea, taiwan, taked, takeing, takhle, talke, tango, tangy, tanto,
tara, tare, tartar, tartt, task, tastz, tble, tbye, teabags, teachin, tearing, teaspoon, technique, technology, teens, tehee, tel, teleport, tellme, tem, temp,
temper, temple, temptation, tenders, termidor, terminated, terminator, terrorists, terug, textlobster, textwhat, thaaank, thaank, thake, thanking,
thankks, thankyoo, thankyooo, thankyyou, thanl, thannk, thanyou, thare, thatnks, thenectarine, theregister, therestaurant, therm, therma,
thermadors, thermator, thermidore, thermometer, thers, thesis, thew, theyve, thezre, thhe, thiago, thieving, thinkin, thise, thisty, thn, thnkas,
thogh, thoght, thoguth, thong, thot, thoughtful, thoughts, thrilled, thrink, thrmador, throne, throughout, throwing, thursday, thw, thyank, tickles,
ticks, tightwad, timed, timmy, tipp, tiramisu, tires, tit, titties, tke, tks, tlk, toaster, toasts, todas, todau, tody, toeat, toen, tof, togheter, toilett,
tommorrow, tomorow, toodle, toodlepip, tool, tools, tooooo, topping, toronto, torque, tort, torta, toss, tot, touche, tounge, tournament, toward,
toxic, tracksuited, tradition, trailer, trainers, traing, trains, tramp, trans, transfered, transport, trap, tras, trauma, travelling, trays, trio, trippy,
trombone, tropical, trot, troubled, trucks, truely, tryig, tsk, tthe, tto, tube, tug, tums, tuna, turd, turf, turing, twirl, twist, twit, twitch, tym, tyo,
tyvm, uch, uf, ufo, ufrom, uhhhhh, uhmmm, ulike, unbecoming, uncooked, unction, und, underscore, undo, undressing, unexpected,
unfortunatley, ungry, unhealthy, union, uno, unprofessional, unreal, unsatisfied, unsure, untill, unzip, uour, upo, upper, uppp, upppp, upscale, upt,
uptight, urban, urge, urged, urges, uss, ust, utah, utensils, uuuuu, uv, uyou, valencia, valid, valuable, value, valued, vancouver, variation, varieties,
vat, vbeer, veel, vegatables, vegeable, vegeatable, veggetable, vegitarian, vegtables, vegteble, venice, vera, verdienen, verlaten, veux, vey,
vibrating, vic, vice, victor, vida, videogame, videogames, viet, views, vind, vineyards, viola, viruses, visitor, vista, visting, vitamin, vitamins,
vittles, vo, void, volia, volta, volte, von, vortex, votre, vouch, vroom, vs, vuoi, vuole, vyfajci, waarom, wach, wad, waddya, waffees, waffer,
waffle, wahtever, wain, waistcoat, waiteress, waitess, waitng, waitresssss, waitstaff, waittress, waive, walkin, wana, wander, wandering, wann,
wanne, wanta, warehouse, wargh, warming, wasd, wasteful, watcha, watery, watress, watta, wax, wb, wearin, weat, wednesdays, weekdays, weet,
weiner, weitress, welcme, welco, welcoe, welcomr, welldone, welocme, welp, wens, wernt, wers, wes, whadda, whahaha, whan, wharf, whate,
whatevers, whatsup, whatyou, whatz, wheee, wheeee, wheeeeee, whehehe, whens, wherd, whever, whey, whi, whichever, whining, whipping,
whistle, whitew, whitey, whith, whitw, whne, whoever, whoooo, whopper, whre, whups, whz, wiat, wig, wight, willl, willy, windy, winee,
wineeee, wint, winte, wioth, wisdom, wisely, wished, wiskey, withthe, witness, witte, wizard, wlcome, wlecom, wne, woaa, woaaa, wod, wodka,
woe, wohoo, woke, wolud, womans, wonderfu, wonderin, wonky, woohooo, wooow, wore, workers, worms, worrie, worthless, wott, woukd,
wouldve, wouls, woups, wrapped, wreck, wrestle, wright, wtg, wussup, wuz, wwater, wwould, wyoming, xfd, yaaaa, yaeh, yard, yayy, yeahhhh,
yeay, yeee, yepp, yesd, yesm, yesssir, yesterdays, yesthe, yesw, yeth, yhear, yhou, yi, yiou, yip, ymmm, yogurt, yoi, yooo, yoour, yore, youare,
youbring, youcan, youer, youg, yourselve, yousomething, youwant, youwish, ypou, yuh, yul, yummie, yummmy, yuou, yure, yust, yuw, za, zal,
zaplat, zaplatit, zdar, zeer, zelf, zen, ziet, ziezo, zine, zinfandel, zipper, zit, zith, zombies, zomg, zoure, zummz, zzzzzzz [1] aaaaaa, aaaaaaaa,
aaaaaaaaaaaaa, aaaaaaaaaaaaaaaa, aaaaaaaaaaaaaaaaaaaaaaaaaaaaa, aaaaaaaaaaaah, aaaaaaaaaah, aaaaaaaaah, aaaaaaaah, aaaaaaaahhhh,
aaaaaaaargh, aaaaaah, aaaaahhh, aaaaahhhhh, aaaaaqaaaaaaaaah, aaaaargh, aaaag, aaaahhh, aaaahw, aaaaieeeee, aaaall, aaaarrrgghhhhhh, aaahh,
aaahhhh, aaaiiiiiouuuuuu, aaargh, aaarrrrhhhhh, aaaww, aabout, aafff, aahhaha, aahhahaha, aahhh, aahhhh, aahhhhhhhh, aajaj, aalllo, aam,
aandrang, aangenaam, aanwijzen, aanything, aar, aararrhhhh, aargh, aarh, aaron, aarrghhhh, aawwww, ababa, abandoned, abbey, abbia, abbiamo,
abbreviations, abck, abdominals, abear, abeet, aber, aberry, abestos, abeverage, abide, abigail, ablegen, abooooout, aboove, abort, aborted,
abortion, abosolutely, aboutn, abouttt, abouuutttt, aboy, aboyfrend, abrams, abs, absentmindedly, absoleutely, absolutitly, absoulutley, absulutly,
absurd, abuffet, abusing, abv, abzora, academic, academically, academics, acardi, accapellas, accdnt, accents, accentuate, accepte, accessible,
accidents, accidnet, accomodi, accompaniment, accomplish, accomplishment, accont, accord, accordance, accordingly, accountable, accountacy,
accountancy, accounter, acctually, accurrate, accusation, accuse, aceia, acent, acertin, ach, ache, acheese, achieve, achieved, acident, acidentally,
aciu, acobb, acoffee, acoke, acommadate, acording, acouple, acoustics, acquaitance, acrobatics, acroos, acsadently, actally, actaully, activates,
actrice, actuall, actuallly, actullay, actullly, actuly, actuually, acualli, addicktion, addis, adelaide, adequately, adhd, adicting, adiela, adifficult,
adige, adio, adition, adjust, adjusting, administry, adn, ado, ador, adored, adrees, ads, adswda, adults, adventurous, adverse, adverstise,
advertisements, advertisied, ady, aek, aer, aernt, aerospace, aethelingas, aew, aewwwwwwwww, afet, aff, affair, affarid, affected, affection,
affects, affleck, affluent, affraid, afgeruimd, afghani, afghanistan, afi, afiesta, afilet, afill, afkdlsfnsdjkfgnsdjkgndsfgnsd, aford, afrai, afraif,
afreak, afree, afriad, afrikaners, afta, afternoone, afterrrrr, afterwd, aga, agai, agaibn, agaim, agan, agency, agents, aget, agfter, aggh, aggitate,
aggressive, aghain, aghh, aghhhhh, aghhhhhhhh, agirl, agitated, aglass, aglrang, agnel, agnet, agoraphobic, agreeable, agreeon, agressive, agriled,
ahahahah, ahahahha, ahahahhaha, ahahhaha, ahahhahahaha, ahahhhh, ahahhhhhh, ahahuahauhauhuahuahua, ahauahuahuahuah, ahave, ahcole,
ahd, ahed, ahes, ahev, ahg, ahhaahahaha, ahhbhhhhhh, ahhem, ahhhhhh, ahhhhhhhhhhhh, ahhhhhhhhhhhhhhhh,
ahhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh, ahhhmmm, ahhmm, ahight, ahmm, ahmmered, ahn, ahnd, ahnice, ahole, ahora, ahrd,
ahrgh, ahrhg, ahsta, ahuahuah, ahuahuahuahuahuahuahuahauh, ahuh, ahum, ahurry, ahvent, ahwooao, ahy, aider, aie, aiee, aieeee, aieeeee,
aiieeee, aimable, aimee, aimlessly, aimm, aims, aintseating, airbags, aireal, aires, airnt, airspace, airways, airy, ais, aisle, ait, aiter, ajajja, ajax, aje,
ajej, ajob, ajuicer, aka, akeck, akin, akis, ako, akous, aks, alabamayou, alabamian, alalalla, albanian, albany, albatros, albatross, albeit, alberto,
album, alcaholic, alchie, alchol, alcoholica, alcohols, alcohomolic, alcy, ald, aldo, alebo, alergies, alergisch, alessa, alessandro, alexa, alexis,
alexz, alfredo, alfresco, algarve, algebra, algeni, algo, algorithms, alguem, alguma, ali, alimony, alirght, alistair, alkie, alkright, allaergies, allas,
alld, alle, alleen, allegiance, aller, allergie, allergisch, alli, alliance, allice, allisson, allive, alliyah, alll, alllll, allllll, alllllready, allllrighty, alllora,
alllready, allnighter, allocated, alloraaaaa, allot, allrighto, allrightthas, allt, alluring, almeno, almere, almond, alobster, alonso, alors, alota, alowd,
alphabet, alps, alrady, alread, alreay, alreday, alreeeet, alrghty, alrgith, alrig, alrightanything, alrightwitchoo, alrightyyyy, alrighy, alrigth,
alrigthy, alsace, alsjeblieft, alstublief, altermative, alternate, althou, alto, altough, altought, alvast, alwayshappyandsmiling, amalgam, amans,
amaraca, amaze, amazed, amazes, amazin, ambiguous, ambrosia, ambulance, ambulence, amelia, amelie, ameraica, americanisch, ami, amicable,
amigo, amila, amin, amiright, amirite, amiss, amm, ammo, amongst, amont, amor, amore, amoung, amounts, amour, amoutn, amphibian,
107
amputee, amsorry, amstel, amtrak, amtter, amusement, ana, anaconda, anad, anall, analyze, anan, ananas, ananasa, anarchist, anastasia, anche,
anchor, anchovies, ancient, ancora, andalou, andare, andcheesecake, andcup, andd, anddont, ande, andere, andforr, andill, ando, andorra, andre,
andrebbe, andrwq, andthank, andwhite, andwot, andy, anecdote, anerican, aneything, angela, angelas, angels, angers, anglais, angles, angli,
anglicky, anglophones, angrey, ani, aniheim, animalcrossing, animated, animator, anit, anitoxidants, aniversary, anjoyed, ankles, annacheers,
annas, annd, anne, annihilated, anniversary, annmarie, annnd, annnnnnnnyways, annnnoyinggg, annoys, annpyed, anny, annything, anomg,
anonimity, anonther, anonymous, anorexia, anotehr, anotha, anotheer, anothere, anotherone, anotherslice, anotr, anount, anout, anouther, anqche,
anr, anround, answres, anterior, anther, anthersten, anthig, anthony, anthropology, anthung, antialiasing, anticipated, antics, antidot, antigravity,
anting, antipasto, antivirus, antonia, antother, antthing, anty, antyhign, antyhing, anuslickville, anwers, anyfink, anyghing, anyhelse, anyhing,
anyhthing, anyhtign, anyhu, anyhwere, anyime, anykids, anyones, anyonying, anyore, anyr, anyrthing, anyt, anytgin, anythang, anythiing,
anythikng, anythine, anythinf, anythinfg, anythingt, anythinh, anythinig, anythinng, anythning, anytign, anytin, anytrhing, anyuway, anyware,
anywat, anywaya, anywayy, anywayz, anywhoo, anywhre, anywwhere, anzthing, aok, aomething, aon, aouthor, aouuugh, ap, apain, apano,
aparece, apateat, apatite, apeak, apear, apears, apertif, apes, apeshit, apeti, apie, apitit, apitite, apitizer, apitizers, aplace, aplle, apocalypse,
apocalyptic, apolagies, apologetically, apologie, apologiea, apologizae, apologized, aporn, apostle, appaled, appalled, apparantly, apparent,
appassionata, appauling, appaulled, appearing, appease, appelez, apperantly, apperetif, appetiszer, appetito, appetti, appettit, appettito, appitizer,
appitizing, appitte, appl, applause, applebees, applesead, appleton, appliance, applicable, applied, applogies, appolagies, appoligise, appoligize,
appologises, appologys, apppetizer, appreciat, appreciates, appreictae, apprentice, appricate, approaching, approval, approve, approved, approves,
apreciate, apretty, apricot, apricotes, aprons, aprove, apt, aqnd, aqua, aquainted, aquela, arab, arabian, arabic, arah, arboretum, arch, archangel,
archbishop, arches, ardee, areeafafasflkjfa, arefit, aregood, areinterested, ares, arestaurant, areu, areveg, arey, areyoureally, arezou, arg,
argapopolous, argghh, arghh, arghhh, arghhhh, arghhhhhhh, argos, arhggu, arigatou, ariite, arithmetic, arive, arkansas, arleady, arlington, arlright,
armargedden, armpi, arno, arnold, arnolds, aroblem, arond, aroud, arouses, arousing, arrangement, arrangements, arrangment, arrangments, arrest,
arrg, arrgg, arrgghh, arrghh, arriva, arriver, arrives, arriving, arrr, arrree, arrrrg, arrrrggg, arruma, arrumar, arsch, arsenal, arsewipe, arshole,
artard, arteries, arthritis, artichoke, article, artisinal, artists, artwois, aruge, asada, asalad, asald, asapagus, asaragus, asay, asbolutely, ascolto, asd,
ase, asec, aseeeeatt, asenhorita, asf, asgdsa, ashed, ashier, ashout, ashtray, asia, asian, asip, asir, aski, asljdfoasfdas, aslo, asolutely, asoup,
aspapgus, aspaprgus, asparagine, asparaus, aspasgus, aspct, aspects, aspettando, aspirin, aspirine, asrequested, assasasa, assassination, assaulted,
assburner, assembly, assertive, assfuck, assignment, assignments, assim, assint, assis, assisstant, assitance, assitant, associates, assole, assortment,
assshole, asssitant, assured, assuredly, assures, asta, asteak, astonishing, astounding, astros, asumed, asure, aswel, asylum, atart, atb, atble, atcha,
ateak, aten, atend, atention, aterw, atfer, athlete, athome, athotmail, athough, atkins, atlantis, atleiskite, atletic, atmospher, atneskite, atoilet, atree,
atrocities, ats, atta, attacked, attacking, attacks, attenbtion, attendant, attending, attentive, attetion, attmpting, attorney, attraactive, attracted,
attractrie, attuned, auacly, auahuah, auaua, auberge, auburn, audi, auditing, aues, aufs, aufwiedersehen, augen, augh, auhauahuahuahauh,
auhauhauhauha, auhsuahsuhasa, aujourdui, aurinko, aushauhsuahs, auspicious, ausserdem, austrailia, authorites, authority, authorized, autistic,
automat, automated, automobile, autopilot, autos, autosnelweg, autostop, auuahauhauua, auwtch, auzi, auzit, ava, avacardo, avail, availible,
avalilable, avantgarde, avegetable, avenge, avere, avid, avnt, avocodo, avoided, avoir, avontuur, avrei, awaeka, awaitress, awaits, awards,
awating, awau, awaybeer, awaycobb, awayyy, awfulest, awh, awhere, awright, awsorrz, awwwawdawdwadwdadasda, awwwlllllright,
awwwwoaamamamaaaa, awwwwwww, awwwwwwww, awwwwwwwwwwwwwwwww, awy, awyas, axe, axel, ayroufa, aything, azaleas,
azerty, aztecs, baaaa, baaaaaafffffff, baanning, baas, baba, babay, babbo, babee, babi, babv, babycakes, babye, babysitter, babyu, babyyy,
babyyyyyy, bachelors, backride, baddow, badge, badroom, baeryy, baffle, bagascia, bagll, bagno, bags, baguette, bahaha, bail, bailed, baileys,
bailing, bain, bait, baka, bakc, baksu, baldock, baldwin, ballerinas, ballet, ballls, balloons, ballsack, bally, ballz, balugas, bama, bambir,
bambloozled, ban, banada, banan, banananananananananan, bancrupt, bandages, bandit, bangers, bangs, bankone, banks, bannas, bano, baout,
baptized, barbara, barbaric, barbeque, barboy, barcelona, bared, barfed, barfing, barge, barhand, barking, barly, barnaby, barney, barnfull, barolo,
barramundi, barrel, barrimundi, barsitting, barstaff, bartend, bartended, bartenderr, bartendeur, bartending, bartentender, bartenter, bash, bashed,
basi, baskets, basketti, baskt, bass, bastage, bastards, bastartd, basterd, bat, batender, batenders, batheroom, bathing, bathrom, bathroo, batmobile,
bats, battered, batteries, battery, battle, battling, baudillard, bauer, bavaria, bavk, bazillion, bbbbeeerrr, bbbbeeerrs, bbbeeeeeaaaaaar,
bbbeeeeerrrrr, bbbeeesssttt, bbbeerrs, bbbiiiggg, bbeach, bbebe, bbeeeeeeeeeaaaaaaar, bberry, bbey, bbill, bbut, bbwer, bcuz, bd, beable, beaches,
beai, beakfast, bealtiful, bearer, bearguy, bearly, beatifull, beatin, beaty, beaucoop, beaujoulais, beaumont, beautful, beautif, beautifoul,
beautifulest, beautifully, beautyful, bebida, bebo, beby, becareful, becasue, becaus, becayuse, beccy, bech, beck, beckys, becorse, becushhh,
becuz, bedankt, bediening, bedieningze, bedienung, bedroo, bedtime, beeee, beeeeaarrrrr, beeeeeeeer, beeeeeeeerrrrrrr, beeeeeep, beeeeeer,
beeen, beefsteak, beehind, beel, beendating, beepbeep, beeps, beerand, beerbeebbeerbeerbeer, beerbeerbeer, beercv, beerfest, beerg, beerglass,
beerio, beerrrrrrrrrrr, beeru, beerwall, beerwas, beetje, beets, beetween, beey, beeyoth, beforee, begginer, beginnen, beginner, beginnings, begins,
begun, beh, behaved, behin, behine, bei, bel, belachelijk, belches, beleave, belge, belgiu, beliefs, believed, belinda, belize, bellisimo, belqium,
belt, bench, bender, benedict, benefit, bengal, benji, bennigans, bennys, bereer, bererb, berlusconi, bernadette, bernard, bernsteins, berrry, berrs,
berryfull, beru, beryypie, besind, besoffen, beste, bestel, bestellen, bestial, bestiegen, beta, betaald, betch, betrayed, betsey, bette, betties,
beucoup, beurk, beutifal, bev, bever, bevera, beveraves, bevi, bewer, bexs, beyy, bezoeker, bfb, bg, bgeer, bgosh, biaaaaaaaatch, bianco, biatc,
bicchiere, bicus, bids, bienvenue, bife, biff, bifjdskljaskl, bifore, bigdavejob, bigga, bigge, bigggggggggg, bigot, bigtime, bii, biie, biiig,
biiiiiiiilful, biiiit, biiite, biina, bijna, bijten, biking, bilico, bilingual, billionaire, billionth, billit, billlll, billplease, billthank, bim, bind, binding,
binds, bins, biochemist, biomedical, birng, birthdays, biscus, bish, bisiness, bisness, bisquits, bissern, bist, bistec, bistrol, bistromath, bitain,
bitchhh, bitchin, bitcj, bitee, biteee, biten, biteoo, bitey, bitian, bitters, bittoo, bittt, biug, biz, bizkitz, biznatch, bizzare, bjus, bjuxx,
bkeaaalallllghhhhhh, bklyn, bla, blablabla, blackbeef, blackbr, blackeet, blackness, blade, blair, blames, blaming, blanco, blandly, blarf, blarg,
blashphemer, blasphemy, blatantly, blaue, blaughhhhhhh, blazed, blea, blease, bleeds, bleeeeaaaargghh, bleeeech, bleeeehchchch, bleeping, bleer,
blehchchhchc, blehh, blehhh, blende, blenders, blends, blessings, bleuettart, blewnder, blijft, blike, blindees, blindly, blinked, blinking, blinks,
blitzed, bload, blocik, blockbuster, blocked, bloede, bloemetje, blok, blokes, bloom, blooody, bloouse, blork, bloss, bluberry, bludgeon, blued,
bluesnews, bluetoast, blumen, blup, blza, bmw, bnegh, bner, bnno, bno, boa, boardwalk, boardyes, boared, boast, bobb, bobbling, bobby, bobetta,
bobette, bobies, bobo, bocks, boddingtons, bode, bodyshot, boeaaach, bog, bogoulet, bohemia, boil, boiling, boire, boise, boisson, boitch, bola,
boll, bolo, bolt, bolttle, bom, bombay, bombs, bonanza, bonapetit, bonding, boner, bong, boniduca, bonita, bonjourno, bonn, bonnetje, bonnie,
bonsoir, bony, boobiee, booga, booger, boogy, boohoo, boohoohoo, booking, booklet, bookmark, bool, boom, boonon, boooked, booooo,
boooring, boooze, boos, boose, boot, bootay, bootoms, booyah, booyakashah, bop, bor, borad, borat, bordeau, boreddd, boredness, boreed, borin,
boris, borke, borked, borocolli, borr, borring, borrowed, borrred, bossake, bosse, bosses, botch, botched, bothered, botom, botong, bototlel, bots,
bottl, botto, bou, boucing, boudboux, bough, bouh, bould, boulders, bouncing, bouncy, bound, bounder, boundries, bouqett, bour, bourgelet,
boutyou, boxing, boxy, boyfrend, boyfrien, boyish, boysonboys, boyyyy, boyz, bp, bq, bqck, braaainnnsss, brace, bracelets, braces, bradley,
bradny, brag, bragging, braiiiiinnnssss, braille, brainnnss, brainnnsss, brainss, braintrust, branch, branddy, branded, brandelynne, brandfree,
brandi, brandz, brang, brasileiros, brasilia, bratz, brauchst, braucht, braun, brautvurst, brautworst, braw, brazilliam, bre, breadmeats,
108
breakdancing, breaker, breakfeast, breasties, breastsis, breathalyzer, breather, brecfast, bred, breed, breifly, brek, brekfast, brekfest, brenden,
brethren, brewmaster, brews, brewsky, brfore, bri, briand, bridges, bridie, brief, brig, brigade, brighest, brightened, brightly, briliant, brillant,
brilled, brimming, brind, bringem, bringin, bringit, bringyou, brink, bristol, britan, britannica, britches, britin, brittish, brittney, brke, brl, brnady,
broadband, brob, brock, broiler, brokenarabic, brolly, brookland, broth, brothel, brotherly, brough, brougth, brow, brownie, brownish, browns,
browsing, browwiasjakobsky, broyljgfgi, brraaaiiinnnnnsss, brraaainnnnnsss, brraiinnnss, brrr, brrraaaaiiiiinnnnsss, brrraaaaiiiinnnnnnssss,
brrraaaaiiiinnnnsssss, brrraaaiiiinnnnssss, brrraaaiiinnnssss, brrraaaiinss, brrrb, brrrraaaaiiiiiiinnnnssss, bruges, brunette, bruni, brunt, brush,
brushy, brutal, brute, brutish, bruxelle, bruxels, bry, bs, bstard, bt, bte, bthx, btu, bubbah, bubbas, bubonic, bucco, buchanas, buckaroos, bucki,
buckinghamshire, bucko, buckoo, buddhist, buddys, budlight, budro, buds, budwiser, bue, buehehe, buen, buena, buenes, buetyful, buff, buffett,
buffoon, buffy, bugged, buggin, bugging, buiding, buidling, buil, builders, buildings, builds, buildup, buisiness, bulb, buldozed, bulgaria, bulky,
bumber, bumblebees, bumbling, bumfuck, bummed, bumper, bunched, bundinha, bungholio, bunking, buonasera, buongiorno, buono, bur, burb,
burde, burgar, burgerking, burgundy, buried, burnssss, burping, burpr, burps, burried, burritos, burro, burrrrppppp, burrrrrrp, burt, bury, bused,
bussy, busting, bute, butive, buton, butsecks, butsex, butte, buttercakes, butterscotch, buttersworth, buttery, buttface, buttho, buttiamo, buttoncall,
butts, butu, buu, buutrm, buuu, buuuuech, buuuuuurrrrpppp, buuuuuurrrrrrrpppppp, buuuuuuurrrrrrrppp, buuuuuuuurrrrrrbbbbbbb,
buuuuuuuurrrrrrrrrppp, buvo, bux, buyin, buzerovat, buzz, buzzed, bveacuse, bveer, bvsgfiwufvwivsnbf, bwahahahahaha, bwahhaa, bwhaahaha,
bwhahahahaha, bwoof, bwurp, bych, byeaz, byeeeeee, byeeeeeee, byessh, byeybye, byhbye, byla, byrawkiospartinenoawayvskovka, byre, bytes,
bytey, byyee, byz, bz, bzor, bzz, caal, caan, caanaaadaaaa, caarrroooooo, cabarnay, cabb, cabelo, cabernetish, cabeza, cabinets, cables, caboose,
cabrao, cabritone, cabrjona, caccia, cactus, cad, cadbury, cadeau, cadendo, caek, caening, cafeteria, caffeine, caffiene, caffine, cafko, cage, cahed,
cahrge, cai, cair, cairns, cairo, caisse, caitlen, caitlin, caixa, caj, cakea, cakec, cakeeeee, cakeeeeee, cakeey, calc, calculating, caldor, califonia,
califormia, californi, californian, califronia, callate, calld, callin, calll, callu, calmer, cama, camaron, cambodia, cambridelike, camden, cameron,
camon, camping, campo, camus, canadaaaa, canade, canadia, canal, canals, canceled, cancobb, candian, candid, candidate, candies, candle,
candlelight, candys, canget, canibals, canmake, cann, canna, cannable, cannibals, cannt, canoeing, cansee, cantr, cantt, cantyou, canyou, caorline,
capacity, capire, capische, capitalist, capitals, capitel, cappucino, capslock, captains, captivating, captive, captured, caqt, caqtch, caracas, caralho,
caravan, carb, carbage, carbonada, carbs, carcass, cardapio, cardigans, carefrl, caregiver, carer, carev, carful, carfulyu, cargo, carid, carl, carlene,
carlisle, carlo, carm, carmel, carnations, carnaval, carole, carolines, carreer, carribean, carribian, carrier, carriers, carte, cartires, carzy, cas, casher,
cashews, cashflow, cashmoney, casile, cass, cassa, casserole, castumers, casual, catalog, catalogue, catches, catchy, categorize, catering,
cathedrals, catherin, catherine, cathline, catnip, catnt, causeyou, causinga, cauze, cava, cave, caviar, cavity, caw, cawk, cazza, cazzo, cazzoooo,
cbobb, cbrb, cc, ccak, ccccc, ccccccccccccc, ccccombo, cccrrryyy, ccheescheese, cchery, ccofe, cdant, cdnt, ceasar, cease, cecha, ceck, cectarine,
ceeeeeeerto, ceesecake, cegep, ceka, cekaj, celebrated, celebration, celebrities, celiac, cello, cellphone, celtic, cement, censor, centen, centerpiece,
centerpieces, centre, centres, cept, cercando, cereals, cerel, ceremony, certailny, certainaly, certainely, certainily, certainley, certainment, certaly,
certasinly, certificate, certo, cervante, cervene, cerveza, cervezas, cervice, cesars, cesspool, cetainly, cette, ceviche, ceylon, cfa, cfo, cfome, cgj,
chablis, chachi, chad, chai, chaiir, chaimpains, chain, chaird, chaits, chaking, chal, chalboard, chalevai, chalfonts, challenged, chamagne,
chamgange, champagn, champaine, champaines, champaing, champange, champean, champeign, champgane, championship, chamring, chan,
chandelier, changs, channels, chanpanh, chapmagne, chapo, chapter, charade, charching, charcters, chardonnary, chardonney, chared, chargeed,
chargemr, charicters, charing, charlene, charmed, charms, charred, chartge, chatabotta, chatboot, chater, chateua, chatter, chatterbox, chaufeur,
chavez, chaya, chcete, chck, cheaf, cheapskates, chear, cheated, cheater, cheats, checando, checf, chech, checkbook, checker, checki,
checkkkkkkk, checknow, checkon, checksh, checl, checque, cheder, chee, cheeche, cheede, cheeeeeeeeeeese, cheeeeeesseeee, cheeesecake,
cheeldren, cheepskate, cheered, cheerful, cheerleader, cheerleaders, cheerss, cheerz, cheesacake, cheesburger, cheesburgers, cheescase, cheescke,
cheeseburgers, cheesecack, cheesecaek, cheesecahe, cheesecakecheesecakecheesecake, cheesecakeeee, cheesecakepietart, cheesecakeplease,
cheesecakw, cheesecfke, cheeseckake, cheesedick, cheesekace, cheesekake, cheesepie, cheesepies, cheeses, cheeseseak, cheesey, cheeshake,
cheesing, cheesse, cheesy, cheetahs, cheey, cheffs, chei, cheking, chekk, chelby, chelle, chem, cheme, chemical, chemically, chemicals,
chemistry, cheper, chequew, chercher, cheremoya, chereooke, cheres, cherrie, cherrycake, cherrycheese, cherrycheesecake, cherrypies, cherrz,
chesapeake, chesepie, chesk, chesseburguer, chessecace, chessecak, chessecakes, chessecale, chetamal, chewer, chewrry, chex, chheeeerrrs, chiar,
chiavi, chic, chica, chicag, chice, chickita, chickne, chigao, chihuahua, chilax, childerens, childern, childr, childreen, childs, chilean, chiles,
chilled, chilli, chillout, chilly, chin, chinchilla, chinezian, chinise, chip, chique, chirsyu, chiseled, chit, chitarra, chloe, chmapagne, chmpaingne,
cho, choca, chockofock, chockolate, choclat, choclate, choclit, choco, chocolade, chocolat, chocolaty, chocolcate, chode, choic, choicecoffee,
choiced, choiche, choicing, choie, choisi, chomppppprprprprprrrr, chompy, chong, choooo, chooooo, chooooocaaallatttee, choooooo,
chooooooooooooo, choppa, chopped, choppyyyyyyyyy, chopsticks, chopy, chore, chores, chosing, chossen, chowed, chr, chrbtom, chrging,
chrysler, chrzz, chseecake, cht, chu, chubby, chucked, chuckling, chucks, chuckwagon, chucky, chuckyhacks, chugs, chuis, chum, chupa,
chutney, chuuu, cia, cibb, cicken, ciela, cigar, cigaret, cigaretes, cigarett, cigerette, cigh, cilantro, cime, cimpulsive, cincinnati, cinco, cinnamon,
cinque, circle, circulation, circumstances, cirtainly, cislo, cisnice, cisnici, cisnika, citchen, citizen, citrus, civilized, cjayc, cjeese, ckayc, cke, cker,
ckj, clacker, claen, claim, claims, claire, clambake, clams, clapclapclap, clara, clarence, clarissa, claroo, clarrissa, classics, classified, classmaster,
clatz, claustrophobic, claws, cle, cleaan, cleanin, cleanly, cleans, cleanse, cleavagechick, clen, clenku, clepto, cleve, clicke, clicks, clientelle,
clietes, cliffs, climaxes, climbing, clincky, cling, clinically, clocks, clode, clody, clogged, clogging, cloi, cloned, cloning, closedf, closeted,
clothed, cloths, cloudy, clowns, clues, clumsiness, cluttering, cme, cmg, cmoe, cmonn, cnery, cnn, cnoto, coagulated, coals, coasteau, coat,
coated, cobbis, cobble, cobbsalla, cobbsallad, cobe, cobler, cobsalad, cocacolla, cocholate, cockboy, cocked, cockface, cockmytail, cockney,
cockroaches, cocksauce, cocksucking, coconut, codes, cody, coe, coeur, cof, cofcourse, cofeee, coffeecake, coffeeeee, coffeeshop, coffef, coffey,
cofffeee, coffi, coffiie, coffu, coffy, cognitive, coherent, cohoice, cohuntr, coifeurre, coin, coincidental, coins, coisa, cokane, cokels, cokes, cokie,
cokies, cokkie, coks, coktail, col, coldd, colde, coldest, colds, colegge, colision, collage, collective, collects, collee, colleg, collegeas,
collegeboard, colleghe, collegue, collisions, collission, coloco, colombian, colonel, colonial, colonies, colonized, colons, colors, coloured,
colours, columbia, comany, comatose, comb, combb, combi, combinations, combining, combust, combustion, comea, comeagian, comedic,
comeee, comein, comeon, comercial, comeu, comfier, comics, comig, comign, comii, comiing, comilao, comingright, comiung, comliment,
comliments, commands, commend, commenting, commerce, commercial, commercially, commiiinggg, commis, commissioner, commited,
commitment, commitments, committing, commonly, commonplace, communicate, communications, comon, compagny, compatible, competition,
competitive, compiling, compinsation, complainer, complaing, complainin, complains, complaint, complemarariy, complemenary,
complementery, complementry, complemetery, complemtary, complentary, completes, completing, compliaments, compliance, compliemnts,
compliemtnary, complimented, complimenteray, complimentry, composed, composer, compound, compounds, comprehend, comprend,
comprende, comprends, compri, compromise, compromising, comps, compue, computadora, computoers, comr, comrad, comrade, comster, comt,
concentrate, concerns, conciderate, concious, concise, conclusion, conclusions, condo, condom, conducted, cone, conference, confess, confident,
confirmed, confit, conform, confundo, confuseing, confusi, confusin, confusiong, confusions, congatulations, congo, congratulate, conic, conm,
conme, connaseur, connery, connetion, connie, conning, conniseur, connoisier, conscience, conseious, consequence, consequences, considerable,
109
considers, consilerous, console, constant, constantly, constitutes, constraints, constricting, constructed, constructive, consued, consulting,
consumers, consuming, contained, contaminated, contamination, content, contenta, conter, continent, continental, contingency, continoue,
continu, continues, continuez, contractor, contractors, contradictory, contributing, contribution, controlled, contruct, convain, conveniently,
converted, convesation, convict, convinent, convos, convulse, coocaracha, coock, coocked, cood, coofee, coofeecoffcoofee, cooffee, coofffffeee,
cookers, cookery, cooki, coolaid, coolector, cooled, coolness, coome, cooming, cooome, coooollllll, cooosinard, cooper, cooperating,
cooperative, coorse, coosen, coosinard, copacabana, copeter, copious, coplete, copliments, copmpanynice, copping, coppola, corbin, cordial,
cordoroy, corect, cork, corkscrew, corncern, corndog, cornered, cornfields, corny, corporate, corporation, corr, corrct, correcctly, corret, cosin,
cosmo, cosmogony, coss, costermer, costo, costuner, cottage, cotte, cotter, cottonpicking, couch, coudent, coughed, coughing, cought, coulda,
couldent, couldn, couldwe, counfuse, counrty, counseling, countertop, counting, countrie, countrybumpkin, countrybumpkinsevenfoureight,
coupled, couples, cource, coures, courese, courser, courtesee, courts, cous, couser, cousre, coustomer, coustumer, cout, cove, coworker,
coworkers, coze, cozy, cpbb, cqke, cqre, cra, cracka, cracked, cracking, crafted, crafting, crai, cram, cramped, cramping, cramps, cranberry,
cranial, cranked, crapped, crappivers, craps, crashing, crasy, crawled, crawlin, crayz, crazed, crazies, crazsy, crazypeople, crazyy, creame, creapy,
creater, creates, creepin, creey, creme, crest, crevettes, crevices, crew, crick, cricket, criing, crilleed, crime, cripple, crisps, crispy, critical,
critically, criticisms, crizz, crob, croix, crossed, crossroads, crouch, crowdad, crowding, crown, crrey, crucified, crumbs, crumpets, crunch,
crustacean, crustesian, crutch, cruton, cryptic, crystalina, crzz, cs, cso, ct, ctarine, ctrl, cual, cuantos, cuase, cube, cubist, cuddlier, cue, cuesta, cui,
cuisanart, cuisart, cuisnart, cully, cult, cultivated, cultures, cumbersome, cummin, cumming, cumon, cumonnnn, cumshots, cunningly, cunty,
cuoca, cuoco, cupa, cupboards, cupcake, cupstacker, curacao, curiousity, currency, curricula, curriculum, curry, curtain, curtains, curteous, curve,
cushty, cussing, custard, custermers, custmaro, custo, custody, custoer, custoi, customary, customerlol, customerrsssssss, customise, customr,
custormer, custumor, custumore, cuter, cutes, cutomer, cutre, cwater, cwhat, cxountry, cyaa, cyaaaaaa, cyanide, cylindrical, cylon, cyninga,
cynthia, cyuww, daaa, daaaaaaaaaaaaaamn, daaaaays, daaaaayyyyoooo, daaaayyyoooo, daaang, daaayyyyoooo, daar, daarna, dabord, dadadam,
dadmakes, daffodil, daffodills, dahh, dahling, daiquiri, dakilo, dakota, dalad, dallors, dalmation, dalring, damagingour, damd, dame,
damfdsujuvuvkbshgjfksjlkfjghaaajdlkhkjasnzbcmnbzvcmnzbvmncb, damien, dammint, damnable, damners, damnnnn, danc, dancar, dandelions,
danemark, danged, danger, danicng, daniela, danika, danish, dankeschoen, dankhje, dankje, dankjewel, danks, dankuu, danm, dann, dans, dansen,
dansk, dap, daphne, dapper, daquri, darbo, dareye, dargent, daring, darjeeling, darkness, darlen, darren, darwin, daryt, dashers, dasie, dates,
datew, dauernd, daug, daugiau, davaj, davexxx, davina, davvero, dawn, daydream, dayis, daysh, dayu, daz, dazzle, dba, dd, dday, ddcide,
ddddddd, ddddiiiddddnnnttt, ddeecafff, ddi, ddoing, dduuhh, deak, deals, dealt, deart, deary, debeating, debris, dec, decadance, decarating, deci,
decides, decie, deciede, decieded, deciso, declaring, decorate, decorated, decoratih, decorating, decreases, dedication, dedichi, deduct, deducted,
deedee, deeeeelicious, deefer, deepest, deepurplesea, default, defeating, defeats, defense, deffinitly, deffintley, defiantaltly, deficile, defies,
definatley, define, definetely, definetily, definiria, definite, definitions, definitivamente, defo, defrosted, deftly, degtineles, dehors, dehydration,
dein, deinking, deiplsy, deirdre, dejouir, dejur, dekuji, delcicius, delectible, delej, delema, deleted, deleware, deli, delic, delicacies, delicate,
delicias, delicicious, delicio, delicioous, delicioso, deliciously, deliciousss, delicioussssss, delicisous, deliciuos, delicoius, delictable, delightfull,
delilcious, delious, delire, delishious, delishous, delishus, delisioso, delisos, delisous, deliver, delivering, dellorkin, dellux, deloris, delt, deltine,
delue, delusion, delux, deluxe, demad, demeanor, demise, democracy, democrat, demolay, demons, demonstrate, demure, denare, denis, denise,
denist, denn, dennis, dennys, dental, dentistryyyy, deny, depend, depending, depens, depiction, depositing, depotism, depp, depravity, depressing,
deputy, deranged, derekdaisey, derest, deret, derivative, derivitives, derp, derriere, derrr, desaid, desc, descecrated, descision, described,
describes, description, deseart, deseat, deseert, deserrrrt, deserted, desertrs, deserves, deserving, desiato, deside, desided, desidera, desighn,
designated, designe, designing, desiray, desired, desirez, deskth, desnt, desolate, desolee, desperate, despise, desrert, desse, desserti, dessertje,
dessery, dessetr, dessets, dessrt, dessrts, destert, destined, destract, destroy, destroying, det, detached, detail, detailed, detecition, detection,
detective, detector, determine, determined, detroit, detto, deucth, deupty, deutsche, deux, devasted, develope, devi, devilish, devo, dezert, dfdsa,
dfg, dfs, dfw, dgf, dgjf, dgoin, dh, dhe, dhrtgs, dht, dhtat, diamond, diamondbacks, diamonds, diane, diaper, diarrer, diasies, dica, diches, dici,
dicjs, dickbeard, dickhead, dickheads, dickheed, dickinson, dicksucker, dictate, diddly, diddy, dided, diden, didi, didint, didntunderstand,
didntyou, didny, didyou, didyoy, didziausias, diediedie, dietro, dif, diffculty, diffe, differ, differeent, differen, differences, difficultie, difficulty,
diffivcult, diffucult, difirence, digestive, digger, diggin, digging, digitally, dihydrogen, diidnt, dike, diky, dildo, dill, dillars, dille, dillema,
dillydally, dillys, dim, dimmer, dimwit, din, dina, dindt, dined, dineer, dinener, dinenr, diners, dines, dingaling, dinher, dinna, dinne, dinnerware,
dinnner, dinosaurs, dio, diooo, dioooo, dioooooo, diplomatic, dipper, dipping, dipset, dipshit, dir, directement, directions, director, direkt,
dirnking, dirrrrty, dirrty, disabled, disagrees, disapered, disappea, disappered, disapproval, disapproves, disassociative, disasterous, disbanded,
disconected, disconnect, discounted, discovery, discreetly, discrete, discussed, discusting, diseased, diseases, diserves, disgraced, disgust,
disgusts, disher, dishy, disicion, disigner, disiple, disk, dismissed, disney, disneyland, disorganized, dispenser, displazed, displease, displeased,
disposal, dispose, disquisting, disrespect, disrespectin, disrupt, diss, dissagree, dissapeared, dissapointed, dissappointed, dissatisfied, dissed,
dissert, dissssssgusting, disszzy, distancedinner, distant, distilled, distract, distracting, distraction, distribute, distroyed, distubing, disturb,
disturbed, ditzy, diverso, divertido, divided, diving, divorce, dizendo, dizer, dizionario, dizzle, dj, dknorthkorea, dlae, dlike, dmanit, dmore, dmv,
dmwahahahahah, dn, dne, dnesni, dnot, doar, dobre, dobreeeeee, doc, doch, docters, document, documentary, documents, dodge, dodgers,
dododododo, doent, doesent, doest, doet, doeuvres, dofus, doggies, doggystyle, dogshit, doign, doiin, doiing, doings, doinh, doint, doit, doiung,
dojete, doka, dokaly, dokedy, dokiey, dokkee, dokken, dolacov, dolarov, dolarz, dollard, dollari, dollas, dollers, dollori, dolores, dom, domain,
domdadom, dome, dominicangemini, donals, donated, donc, doncha, dondt, donesol, donn, donnt, dono, donot, donr, dontcha, dontgive,
dontknow, dontn, donts, donttouch, donttry, dontwanna, donty, dood, doody, doofus, doolars, doom, dooo, doooo, dooooomed, doorbakken, doot,
doozo, doozy, dop, dopey, dora, dorien, doris, dorry, dos, dost, dostanu, dostat, dots, doublecheck, doubled, doubleuteeeff, doubly, doubtful,
doubts, doughnuts, dougs, dovere, dovrei, dowm, downed, downing, downloads, downn, downnnn, downstairs, downtown, downunder, dpo,
dpois, dqnitm, dracula, drafts, drafty, dragged, dragon, dragqueen, drags, drained, dramaaaa, dran, drankz, drapes, drat, drawers, dray, dre,
dreadful, dreamed, dreaming, dreamster, dreamy, dreg, drenk, dresseing, dresser, dressers, dresses, dressign, dressrt, dribe, dribk, drie, dried,
drift, drifte, driiiiiink, drimk, drinck, drind, drinkers, drinkgin, drinkie, drinkin, drinkings, drinl, drinlk, drinx, drip, drips, drirnk, driunk, driv,
drivebys, driven, drivers, drivethru, drnk, droid, droits, dronk, dronken, dropdown, dropkick, drrunkkk, drudgery, druggy, druhe, druk, drukn,
drun, drunkard, drunkenness, drunkkkk, drunkkkkk, drunkship, drunkz, drunmkmcaklermal, drunnkkk, dsalad, dsfg, dsg, dsl, dsssss, dt, dthx,
duckbeard, duct, duddt, dudes, dudre, dududum, duff, duhh, duhhhhhh, duhj, duing, dullest, dumbkopf, dumby, dumdidumm, dummies, dumping,
dumpster, dumster, dundundun, dunnn, dunoe, dunt, dupont, dur, duran, dure, durrty, durty, dusk, dusnt, dusst, dutc, duthch, duties, duur, duurt,
duuuuur, duvod, dvd, dvours, dvr, dwarf, dwednesday, dweeb, dwell, dwelling, dwest, dww, dx, dxa, dyas, dye, dyed, dyou, dysentary, dyslexic,
dzrt, eaaaagrrrrhhhhh, eaat, eachoth, eady, eagerly, eally, eampty, eariler, earler, earnestly, earnings, earnt, earthling, earthlings, eas, easat, easiet,
eastenders, easting, easyer, eate, eaters, eatingg, eatings, eatlets, eatsings, eatting, eattt, eattuing, eave, eawt, eay, eb, ebbeeer, ebbr, ebee,
ebeerrressssb, eber, eberythink, ebough, ebr, ebtree, ebtter, ebut, ec, ecae, ecak, ecake, eccellent, eccoti, eccuse, echoes, echte, ecosystem, ecstasy,
ecumenical, ecver, ediable, edinburgh, edition, editor, edoing, edredred, eduardo, eeasily, eeee, eeeee, eeeeeat, eeeeeeaaaaat, eeeet, eeeexcellent,
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eeek, eeeuww, eehm, eejit, eenglesh, eening, eens, eensproeven, eerder, eerst, ees, eevrything, eew, eeww, eewww, eexcuse, eff, effected, effet,
efficiently, effin, efforts, efilet, eg, ege, eget, eggcellent, eggos, eggroll, eggroww, egh, eglantines, eglish, ego, egyptian, ehave, ehe, ehehehe,
ehehehhe, ehhmm, ehhr, ehite, ehlp, ehn, ehr, ehuheue, ehyt, eie, eigenlijk, eigentlich, eighties, eighty, eii, eikes, eill, eins, eipei, eipeiep, eipep,
eipweipeipewriawte, eis, eisai, eist, eit, eith, eithe, ejection, ejh, ejhye, ejsy, eklll, ela, elanen, elaphant, elaven, elbow, elderberries, election,
electrician, electrolyte, electronic, elememtary, eleviating, elise, elites, elizabetha, ellen, ellington, elloo, elmo, elo, eloisa, eloquently, elose, elp,
elrob, elsa, elsee, elsefor, elser, elsew, elsewhere, elsr, elsse, elusive, emailed, emanating, emanuel, ematter, embarased, embarass, embarrassing,
embrassing, emial, emike, emiliano, emilianos, emilz, emmo, emotion, emotions, empathize, empety, employ, employs, emplyee, emprium,
empties, emputy, ems, emtye, enchanted, encheuligung, encountered, encourage, encouraged, encouraging, encyclopedia, endless, endorphins,
endorsement, endued, enesign, eneything, eng, engeenear, engels, engineered, engineers, enginering, engleesh, engles, engloish, englsh, engly,
enhance, enhancement, enhoy, enhoying, ening, enj, enjjoy, enjoed, enjoi, enjoyes, enjoyl, enjoysitting, enjoyyour, enjoyz, enlightened,
enlightenment, enligsh, enloyed, ennjoy, enogh, enojy, enrosco, ent, entangled, entanglement, entao, entendo, enterprenurial, enterprise,
enterprises, enthing, enthusiastic, entice, enticing, entirelyy, entonces, entrance, entrepreneur, enum, envious, envy, enythink, eoeoeoeo, eone,
eopie, ep, epeeing, epeie, epiei, epieie, epiep, epiepie, epileptic, epipei, epipen, episo, episodes, eplease, epoieiee, epoieoooie, equality, equates,
equipped, equivalent, equivalnet, era, erady, eradyt, erage, erase, erb, erbij, erection, ered, erenity, erg, ergens, erhem, erhm, erinnnn, erk,
ermmmm, ernest, errmmm, erroe, errors, errrr, errrrdunno, errrrm, errrrrr, erthe, eruit, erupt, escap, escapade, escaping, escoffier, escoffiers,
escorrt, escorted, escuse, esdfsadfdsf, esles, esmes, esoteric, espacially, espaghetty, espanha, espeacially, espeically, espere, ess, essay, essence,
essentially, essertwould, established, establishme, esteak, esteemed, esti, estou, estudo, etait, ete, etea, eth, ethe, ethier, ething, ethiopia, ethniticy,
etip, etudes, eua, eugenios, euj, eunich, eup, europeans, euu, euy, euyep, euyrt, eva, evah, evaluate, evaporated, evaporating, evasion, eveery,
eveil, evenign, eveniong, evens, eventjes, everning, everpresent, everybodys, everydays, everyhitng, everyhting, everying, everyoplate, everysing,
everythiing, everythinbg, everythung, everyti, everytin, everyting, everyuwhere, everywher, everyything, everzthing, evette, eveyday, eveything,
evidenced, evident, evidently, evleven, evr, evryones, ewa, ewith, ewwe, ewwwwww, exaccctyly, exacly, exactally, exactlz, exaggerating,
examples, exceed, excellen, excellence, excellente, excellently, excellet, excellt, excepted, exceptin, excercises, excetion, exchequer, excidently,
exciiting, exclent, excllent, exclude, excoos, excsue, excsues, excue, excuise, excuisit, excuseer, excuseme, excuser, excusme, excuuuse,
excuuusee, excuze, exectly, execute, exei, exercise, exercising, exercize, exercsing, exeter, exhange, exhusted, exicting, existed, existence,
existent, existential, existenze, exiting, exiyt, exl, exmissus, exorbitant, exorciced, expansion, expe, expectation, expecte, expenisive, expense,
expenses, expensivest, expentional, experiance, experiement, experiences, experiencing, experiments, experince, expieriec, expired, expirement,
explanation, explantion, expletives, explian, explicit, explodes, explodindo, exploit, exploited, exploiting, expo, exponential, exposed, exposing,
express, expressions, exsactly, exsplain, exsuse, extastential, extension, extensive, exterminators, extinguish, extort, extract, extracurricular,
extracurriculars, extravagant, extravagantly, extravigantly, extreme, extremley, eyebrowless, eyeing, eyyyy, eyyyyy, eyyyyyyyy,
eyyyyyyyyyyyyyyyy, faa, faaaaag, faal, faanxc, fable, fabric, fabrizio, facade, facciamo, facciano, faccio, faced, faceless, facelift, facile, factors,
fad, faggerd, faggy, fagit, fagmo, fai, fails, faints, fainy, fairtrade, fais, faisiez, fajcis, faking, falando, falet, falled, falo, falow, false, falsified, falt,
famed, famiashed, familys, fammiliar, fanatic, fance, fancey, fandango, fandiddlyastic, fands, fane, fanks, fanny, fanstastic, fanta, fantabidousy,
fantabydousy, fantasticc, fantastika, fantasy, fantsatic, fantstic, fanxx, faptastic, fareham, farewells, farewelss, farming, farrtt, fartknocker,
fashion, fatback, fater, fathom, fatigue, fatt, fattening, fatto, fattt, faty, faulty, faux, faveourite, favoprite, favorable, favore, favority, favourites,
favours, favourtie, favriote, fawetaer, fay, faz, fazer, fbi, fdhkjgg, fdsgf, fean, fearsome, feat, feather, feathers, feature, feb, february, feeed,
feeeel, feelen, feeler, fees, feets, feiend, feldmansteinoliver, feliot, fellas, fellit, felony, femaile, feminine, fencing, fend, fender, fengshi, feom,
feral, fermenta, fermentation, fern, fernando, ferry, fervor, fery, fest, fetal, fetching, fetish, fetishes, fever, fews, ff, fffin, ffine, ffs, ffxi, fg,
fghjklbitchhjkl, fgoing, fgrdjh, fgriend, fh, fiancee, fianlly, fibne, fibre, fibrous, fico, fiction, fictional, fiddlesticks, fiddly, fidgety, fidh, fie,
fierce, figh, fighters, fights, figlot, figre, fih, fiiiineee, fiilit, fika, fil, filegt, filetand, filetes, filetmigon, filetmingon, filett, filey, filho, filiot, filket,
filley, fillipe, filllet, fillot, fillu, film, filot, filrt, filter, filtered, filthest, filue, filuge, fimnished, fin, finale, finalmente, finaly, finances, financials,
financiers, findest, finer, finesse, fineto, fings, fingurs, finis, finisd, finisged, finishe, finishes, finishhedd, finisht, finna, finnally, finnish,
finnshed, finr, finshing, finsihed, finy, fionished, fiori, fir, firefighter, firefox, fires, fireworks, firsherman, fiscal, fishf, fishsticks, fistion, fists,
fitte, fitting, fitty, fiufjikvujfvjfejdv, fiurst, fiut, fiveee, fixin, fixings, fiyah, fiydtd, fjglk, fjlkh, fla, flabbergasted, flag, flagstaff, flaky, flamable,
flamand, flambe, flame, flaming, flamingschoolbus, flandre, flare, flashbacks, flask, flass, flatmate, flattened, flatter, flatulent, flaunting, flaver,
flavors, flavoured, flavours, flawed, flaws, flay, fledge, flees, flegh, fleur, flex, flexible, flick, flickering, flie, fling, flinke, flipped, flipside,
flireda, flirtation, flirty, fliton, fllet, floated, floo, floooor, floozy, flops, flora, flordia, flore, flores, flos, flouers, flourescent, flours, flowera,
flowereating, flowerpot, flowersss, flowersw, flowerws, floyds, flozers, flsattery, fluente, fluffy, fluid, flush, flux, flwers, flys, fm,
fmfmghmfmmljhgfmlgllgfmflbgmphgfmhfgmhdfmhdfml, fnarr, fnd, fnish, foam, fobia, foc, focuses, fod, fode, foggy, fogot, foi, foid, foie,
foiled, foils, foind, fois, fokkers, fold, folllow, follo, folloe, folloow, follor, followe, followin, follows, folloz, follwing, follwo, folowing,
folowme, fom, fome, fomous, fomr, fondled, fondling, fone, font, foodand, foodd, fooddd, fooden, foodo, foodstuffs, foodz, foof, fookil, fooking,
foolow, foooddd, foool, fooooood, foooooooooddddddd, fooot, foor, foos, footsteps, forbid, forbidden, forbidding, forces, ford, forecasting,
forehead, foreign, foreigner, forf, forger, forgets, forgetting, forgor, fori, foriegn, forigive, forizzle, formal, formalities, formed, former, formy,
forogt, forom, forppl, forprotection, forsaken, fort, forth, forthright, fortunately, forwad, forzen, foshizzle, foster, fotget, fotgot, fottiti, founded,
founding, fout, fowless, fowllow, foxxy, foxy, foyu, fps, fqcher, fraaaank, frage, framboesa, fran, franc, franciacorta, francine, francis, francois,
francoise, francs, frankie, fratinizing, fratranizing, frau, fraudster, freaked, freakkkorama, freaks, freakshow, freaky, freally, frech, freebiiiiiird,
freedoms, freee, freeeeeee, freeesh, freeloader, freeman, freemans, freerange, freetime, freeware, freezes, freezin, frehbourgh, freiburg, freinf,
fremedon, frenc, frencais, frenche, frenches, frenchs, frenchy, frendh, frends, frensh, frequently, fresca, freshbaked, freshcaught, freshman,
freshmen, freshness, freshwater, fresno, fretless, frewsh, frewt, frexinet, frick, fricken, fricking, friction, fricton, friedn, frien, frienship, friggen,
frigging, frightened, frightening, friiidge, friken, frikkin, frilled, frineds, frio, friots, frisco, frisee, frish, frist, fritas, frite, frittas, fritz, fromage,
frome, fromit, fromn, fromunge, frontpage, froom, fror, frosin, frot, frotn, frown, frowned, frowns, frrench, frui, fruist, fruitage, fruitbasket,
fruitcake, fruite, frustrates, frut, ftopic, ftritas, ftw, fuciking, fucing, fucka, fuckbag, fucke, fuckhead, fuckign, fuckinh, fuckoff, fucktown,
fuckwit, fud, fudgenstein, fuel, fuer, fugatives, fuge, fugg, fuggs, fughedabotit, fuh, fuhnny, fuint, fujn, fukc, fukin, fukkstein, fukksteins,
fulfilled, fulll, funck, functions, fungi, funn, funnily, funnny, funnt, funnz, funziona, fuori, furious, furit, furnature, furry, furthering, fusking,
fussy, futurama, fuuccck, fwat, fwm, fx, fyn, fysiks, fzendu, fzer, gaap, gab, gabby, gabriel, gagging, gahhh, gaia, gained, gainign, gala, galaxy,
galileo, galima, galiu, galleria, gallery, galley, gallo, gallons, galore, gals, galva, gama, gamay, gambeling, gambler, gamespace, gamiso, gamon,
gan, gane, gangsta, gangstah, ganster, ganzo, gao, gap, gaps, garage, garbaaaje, garbageman, garbago, garbles, garconete, garconte, garde,
gardena, gardenia, gardenias, gartuity, gass, gast, gastronomic, gastronomical, gat, gates, gateux, gathered, gatorade, gatta, gavi, gaw, gawd,
gawjus, gawr, gawsh, gawww, gayload, gaz, gaze, gazzillion, gb, gbf, gblass, gcan, gdgd, geardagum, gebouw, gebruiken, gecontroleerd, ged,
geddoutaherre, gedouguteert, geee, geeez, geeks, geem, geer, geet, geeze, geezer, gefrunon, gegessen, gegrilde, gehad, gehen, gehn, geht, gehts,
geil, gek, geklaut, gelatina, gelato, gelooft, gelous, geluid, gemaakt, genatalia, genau, genders, generating, generation, generations, generosity,
111
generoso, genes, genetic, genetically, genick, genie, geniet, genirosity, genitals, genittalia, genoeg, gent, gentile, gentlman, genuine, geoff, geoge,
geology, geometry, georgana, georges, georgina, georginas, gerald, geraldos, geraldton, geraniums, gere, gerecht, gerente, gergoe, gerhard,
geriau, gerk, germans, germophobe, germophobr, gerne, gesprochen, gestern, gesture, gesundheit, getaway, gether, getr, getsome, getsready,
gettos, getup, getwhats, geuss, gevonden, gew, geworden, gez, gfffff, gfreat, gfu, ggggg, ghdk, ghello, ghey, ghi, ghood, ghrtws, ghte, gia,
giavvani, gibber, gibbered, gibe, giddy, giev, gigantic, giggidy, giid, giiift, giiirlyyy, giimme, giive, giltwork, gime, gimick, gimmee, gimmeh,
gimmick, gimpsuit, ging, gingerity, gingerly, gino, ginormous, gioco, giood, giovanni, girata, girfriend, girft, girldfriend, girlfreind, girlfried,
girlfrind, girlie, girlish, girll, girlly, girrrlfriend, gis, gived, givesh, giveth, givey, gjdkigdfjsg, glaases, glamerous, glamorous, glare, glases,
glaseswater, glassware, glaubich, glazed, glide, glielho, glielo, gliled, glilled, glitched, glitches, glitchy, global, gloomy, glove, glowing, glsses,
glub, glucose, glueybottoms, gluten, gluttony, gmornin, gnaa, gnawed, gnt, gnurk, goa, goad, goalllll, goals, goats, goatse, gobber, gobbledys,
gobi, godamn, godawful, godbey, goddamned, goddamnned, goddbye, goddess, godforsaken, goede, goffyy, gogo, gogogo, goiung, golddiger,
golddigger, goldeneye, goldies, goldman, golet, golfer, golgarat, golgotha, gonnie, gonorhea, goobbbeerr, goobers, goobye, gooday, goodb,
goodbey, goodbyt, gooden, goodf, goodgood, goodie, goodies, goodmornin, goodmorning, goodnes, goodthanks, gooed, goofball, goofed, goofin,
goofing, goog, gooing, goona, gooners, gooobiiee, goooo, gooooo, goooood, goooooooo, gooooooooo, goooooooooo, gooooooooooooo, goos,
gooseberries, goot, gordo, gorg, gorge, gorgeouse, gorges, gorgeus, gorilla, gormet, gorumet, goshdy, gosse, goth, gothic, gotsh, gott, gottabe,
gottahask, gotteen, gottl, gotz, gourd, govern, governa, goving, govnah, goy, gozaimasu, gozillionth, gpa, gpo, grabastic, grabbing, grabby,
graceful, gracefully, graci, gracia, grader, gradimento, grading, gradpa, graduat, graduation, graet, graffic, grafiti, grahm, grains, grammer,
granddad, grandmas, granma, grannie, granny, granoooo, granooooo, granpa, granpappys, grant, grants, grap, grapevine, graph, graphix, grapje,
grapnuts, grappa, grappe, graruity, gras, grasses, grassy, gratefully, gratituty, gratu, gratuit, gratuite, gratuiyu, gratze, gratzi, grave, gray, graze,
grazed, grazias, gre, greaat, grealed, grear, grease, greata, greater, greatnight, greaty, grece, gree, greec, greeeeaaaat, greeeens, greek, greeks,
greenbeans, greenhouse, greenmarket, greensboro, greenville, greet, greeted, greg, gregg, gregio, greiciau, grety, grid, gril, grileed, grileld,
grilledsalmon, griller, grilles, grilling, grillled, grimaces, grimmy, grind, grink, gripe, grits, grizzly, grl, groan, groceries, groentje, grolsch,
groping, gropy, gros, grose, grouch, grounded, grounds, grounf, growl, growling, grows, grrrrrreeeeeeat, grrrrrrr, grt, grubbing, grumbling,
grump, grunt, grunts, gruuuu, grzie, gs, gthink, gtting, gu, guantanamera, guantanamo, guapa, guard, guardi, guards, guarentee, guatamala,
guatamalan, guatemala, guatemalan, gudbye, gude, gue, guees, gueessis, guesswe, gugh, guides, guido, guilty, guinea, guise, gumballhead,
gunits, gunk, gunno, gunses, gurls, guse, guss, gusto, gutamalean, gute, gutentaag, gutentag, gutten, guty, guud, guzzle, gw, gwan, gweesh, gwen,
gyes, haaaa, haaaaa, haaaaaa, haaaaaaa, haaaaus, haaath, haah, haahaha, haahahahaa, haahahahahha, haahahahhahahaha, haahhahaa, haav,
haavvvee, habbit, habbo, habla, hablaron, hack, hadda, hadent, hades, hadoken, hadouken, hae, haers, haette, haff, hafta, hag, hagve, hahaaaaa,
hahaahahaha, hahaahahahahaha, hahaahha, hahahaaa, hahahaaaaa, hahahaaaaaaa, hahahaaha, hahahahaa, hahahahaah, hahahahaahhahaahah,
hahahahahaah, hahahahahahah, hahahahahahahaahahahahahahhahhahahahahahahah, hahahahahahahaahahhahahahahahaha,
hahahahahahahahaahahahaha, hahahahahahahahahaha, hahahahahahahahahahaha, hahahahahahahahahahahaha,
hahahahahahahahahahahahhhahahahahahahaha, hahahahahahahhahahahahahaah, hahahahahhaha, hahahahahhhaha, hahahahhahaha,
hahahahhahahaahahhaahahah, hahahahyuck, hahahawatch, hahahh, hahahhaahaahahaahahhahaha, hahahhahahaha, hahahhahahahahahah,
hahahhahahhahahhahahha, hahahj, hahas, hahasha, haheahaehaeha, hahh, hahhaha, hailing, hailly, haing, hairdresser, hairdressers, hairs, hakan,
halafax, halfdrunken, halflife, halfpast, halfway, halfwit, hallafax, halllo, hallloooo, halloo, halloooo, hallow, halloween, halten, hambre,
hamburg, hamburgo, hammar, hammerd, hammy, hampshire, hamster, handful, handicap, handicaped, handiwork, handouts, handover,
handsmoe, handsom, handsomely, handz, hanger, hankthedrunk, hanson, hanwaiyt, hapan, hapen, hapenned, happier, happiness, happpppay,
happyinternetinghotmail, happz, harasment, harass, harddrive, hardlopen, hardness, hards, hardy, hare, haribo, harion, harpies, harras, harrasment,
harrasseemt, harro, harsh, harty, harumph, harvey, hasj, hasnd, hast, hasten, hatat, hater, hatin, hatte, hauhauhau, hause, hauss, haut, havben,
havce, haveee, haveglass, havesex, havesome, havet, havethe, haveto, havew, havi, havig, havin, havine, haviong, havok, hawaiian, haww,
hawwtt, hawwwwbig, hayyaa, hayyy, hayyyy, hayyyyyy, hazleton, hbamsdjiy, hbave, heaabees, headachesnow, headcold, headlight, headlights,
headstrong, healthcode, healthiest, healthiness, heardof, heared, hearin, heartbreak, heartfelt, heartland, hearts, heathen, heating, heatness,
heavenly, heay, heber, hebt, hecka, hectore, hects, heeby, heeee, heeeeeeeeey, heeeeelllllllloooooooo, heeeehee, heeeeooooooo, heeeheee, heeey,
heeh, heehee, heeheehee, heeheheeheh, heej, heellloooo, heello, heelo, heeo, heerlijk, heers, hef, heffezeissen, hefty, heheeh, heheheeh,
hehehehee, heheheheeee, heheheheh, heheheheheehehehehe, heheheheheheh, hehehh, hehehi, hehello, hehheeh, hehre, hei, heid, heil, heimlich,
hein, heiniken, heinlick, heirloom, heissd, heisse, heisst, heklp, hele, helen, helena, heletijd, hella, helle, helli, hellish, helllllllooooo, helllllooo,
hellllooo, helllloooo, hellllooooooo, helllooooooo, helloand, hellod, helloe, hellohellohello, helloooooooooo, hellop, hellote, helmets, helolo,
helooooooo, helooooooooo, helop, helpen, helper, helpo, helpyou, hemeroids, hemingway, hemmy, hemroids, hengst, hennessy, henrietta, hento,
hera, herassment, herb, hereditary, hereeeeee, hereis, herelater, hereplease, heresa, hereu, hereyou, heritage, herman, hernia, heroine, herpe, herre,
herree, hers, hertfordshire, hertog, herts, heshe, hess, hewre, heyaaaaa, heyhe, heyloo, heyok, heys, heyt, heyyou, heyyyooo, heyyyyy,
heyyyyyyyyyy, hfd, hfdhf, hflkh, hg, hgf, hgl, hhahah, hhahahahhhahhah, hhahha, hhave, hhe, hheeeeee, hhehehe, hhellhfilfihello,
hhellohellopleplesttake, hhere, hhha, hhheee, hhhh, hhhhhave, hhmmm, hhmmrfsd, hicking, hicup, hidden, hideous, hides, hidiho, hierbij, hierna,
hiero, highball, highest, highlight, highlighted, hight, hightlight, hightly, highyl, higine, hihglight, hihih, hihihihhi, hihihihioh, hiho, hiid, hiihii,
hiiiii, hiiiiiiiii, hiiyaaa, hij, hijacking, hilarioes, hillarious, hills, hillsboro, hilo, himmmm, hindsight, hine, hiodentify, hipnotize, hippest, hippo,
hireing, hiro, hispanic, hite, hitewine, hiu, hiuse, hiv, hives, hiyaaaa, hiyas, hiyo, hjappy, hjard, hjey, hjungry, hkl, hlavy, hlllo, hlp, hlpe, hmf,
hmmat, hmmmhhhh, hmmmmlooks, hmmpff, hmnggggg, hn, hned, hney, hnkyou, hobag, hobos, hockey, hocus, hodim, hoef, hoeft, hoeny,
hoeveel, hoezo, hog, hogwarts, hol, holaday, holder, holds, holdyour, holes, holiest, hollands, holle, holocaust, hologram, homarus, homecoming,
homely, homes, homeschool, homeslice, homless, homogenised, honemoon, honeslty, honesty, honeybee, honeybuns, honger, honner, honney,
honny, honolulu, honors, honoured, hood, hoods, hoofd, hookah, hookers, hookup, hooome, hoope, hoorrrnrnnnnnnn, hoort, hoos, hoot, hooter,
hooties, hopefull, hopeless, hopfully, hopign, hoplease, hopper, hopping, hopw, hore, hormonal, hormonefree, horney, hornier, horray,
horrendous, horribel, horrific, horror, horsemeat, horsey, horticulture, hoshia, hostess, hosting, hosue, hotblack, hotcakes, hotea, hotline, hotness,
hotr, hotstuf, hotstuffs, hotsurfers, hotttt, hotty, houdt, hould, hound, houres, hourra, housepainter, housewine, housy, howa, howabout, howday,
howhats, howling, howm, howz, howza, hp, hpe, hpv, hpw, hqving, hrajou, hral, hrat, hrd, hrmmm, hrmmph, hrough, hrrm, hru, hrumph,
hsauhdsu, hsudhsuds, htank, htar, htgrf, htne, htrg, hubby, hubot, huff, hugge, hugh, hugi, hugocurado, huhm, huhuhuhuhu, huih, huis, hulaing,
humanely, humanilism, humanoid, humber, humble, humbug, humerous, humf, humhum, humper, humphrey, hums, hunagry, hunch, huney,
hungey, hungover, hungr, hungru, hungrz, hunni, hunnybuns, hunted, hup, huriy, hurricanes, hurrricane, hurryback, hurst, hury, husahsuahsa,
huse, hush, hust, hut, huu, huugger, huumm, huury, huuum, huuuuuuuuurrrrrrryyyyyyy, huv, huzzah, hva, hvat, hviteqwine, hvitewine, hwaet,
hwehe, hwey, hwich, hwitewhine, hwy, hya, hyacinthus, hyacus, hydrant, hyey, hygenic, hygiene, hygienic, hyou, hype, hypothetical,
hypothetically, hysterics, hyt, iamoo, iamooo, iamoooooooooo, iand, iask, ib, iba, ibll, ibm, ibn, ibu, icedtea, icetea, ichi, ichu, icic, ick, icoming,
icscuse, idag, idaho, ide, ideal, idee, idem, ides, idid, idioms, idioot, idiottttt, idito, idle, idn, ido, idshes, idt, idunno, ieat, ieee, iei, ieip, iel,
iephelp, iepi, iepiep, ier, ies, iesha, iether, ietro, ife, ifee, iffens, ifn, ify, ightup, ignon, ignorme, igonna, igor, igot, igsitent, ihae, ihey, ihi, ihn,
112
iight, iiieeeew, iiii, iiiii, iiiiii, iiiiim, iiiiimm, iipa, iit, ikalt, ikd, ikdyz, ikea, ikn, iknow, ikz, ild, ilike, ilikebeer, ilista, illadd, illbring, illpost,
illusen, illusionary, illustration, ilu, ilusyou, images, imagined, imagining, imallergic, iman, imaoo, imaooo, imbalance, imdone, imean,
imedietely, immeadatly, immedialty, immediatel, immerse, immigrant, immigration, immmmm, immmmmm, immmmmmmm, immunie, imnot,
imo, imovies, impaciete, impact, imparare, impatience, impediment, impedimewnt, imperialist, imperialisten, implants, implemented,
implication, implies, impolite, imports, imposed, impossibile, impressions, improper, improved, improvement, impulsive, impune, imssorry,
imtn, imvu, inac, inactive, inadequate, inanimate, inappropriately, inappropriateness, inapropriate, inaproptriate, inc, incapable, incidently,
incisble, inclusive, incoherently, incomprehensible, inconsiderate, inconsistancy, inconveinience, inconvenie, inconvenienc, inconvenienced,
inconvenient, inconviniance, incovenice, incovinience, incovinient, increase, increases, incurable, inda, indan, indded, indecisive, indeedios,
indeeeed, indeeeedddd, indegestion, independantly, independent, indepth, inderdaad, indicated, indie, indies, indifference, indifferent, indoor,
induced, inefficent, inept, inexpensive, infidel, infiltrating, infinite, influence, informatic, informatica, informations, informative, informs,
infrastructure, infringing, ing, ingame, ingegneria, ingest, ingested, inglese, ingrediants, ingrediences, inhere, inhouse, initiative, inizio, injections,
injoy, injure, injured, injury, ink, inn, innovative, inpecible, inpression, insanity, insatiable, insects, insident, insiiide, insinuating, insisting,
insists, insolence, insomniac, inspect, inspiration, inspires, inst, install, installed, insted, institution, instructions, instrument, instruments,
insubordinate, insubstantial, insults, inteeligence, integer, intellectual, intending, intenet, intentional, intentionally, interactivity, intercepted,
interconnected, interes, interestingly, intereted, interfere, intern, internationallyfor, interpretation, interpreted, interresting, interruption,
intersection, interseting, interwebs, inthusiastic, intoxicated, intoxicationg, intreguing, intresting, introducing, intrusion, invasion, invention,
invest, investigating, investments, investors, invigorating, invisble, inviting, invullen, inyto, iom, ioohh, iorder, iordered, iove, ipair, ipay, ipepi,
ipey, iphuckhorses, ipie, ipod, irack, iran, iraq, irasshaimase, irgi, irony, irrashaimase, irrestible, irretrievably, irritates, irs, isabelle, isdg, isdn, ise,
isent, isfood, isgerkite, isgersite, ishall, isint, islamic, isle, isnectarine, isnit, iso, isolated, israel, isreal, issmall, istart, isthat, isthe, istill, isuggest,
iswhjat, isyours, ita, italia, italin, italina, itall, itallian, itau, itcalled, itching, itfrom, ith, ithink, ithought, iti, itis, itlaian, itlay, itlol, itmay, itoff,
itother, itsaid, itscold, itstuck, itszerin, itto, itunes, itz, iubesc, iubita, iusualy, iut, iuts, iuuu, iva, ivana, ivanka, ivies, iw, iwannafucku, iwant,
iwas, iwater, iwnat, iwne, iwont, iyh, izit, iziy, izza, jackie, jackoff, jacks, jacksons, jade, jaden, jagerbomb, jah, jaheur, jaimerai, jaimes, jaimie,
jajaj, jajajaja, jake, jale, jamecia, jammed, jammer, jammie, jammy, jan, janden, janeiro, janey, jangro, jap, jarne, jarro, jarvis, jas, jasne, jau, jave,
jaw, jawin, jaws, jayden, jaydenn, jayyden, jayyen, jaz, jde, jdnoho, jeanette, jeanluc, jeans, jeck, jedi, jedneho, jednicky, jednou, jee, jeebies,
jeebs, jeee, jeeezzz, jeesh, jeezy, jei, jeje, jelp, jena, jeng, jenga, jenn, jenna, jenne, jennifer, jeresey, jerkl, jerkwad, jerkweed, jerky, jerrys,
jespere, jesse, jessie, jeste, jestli, jetskayla, jetsons, jetty, jetyt, jetzt, jetzyt, jeur, jeverything, jeweez, jewellry, jewisicaldepbpepermint,
jewsicalpeppermint, jeziet, jezis, jg, jhave, jhere, jhh, jhon, jhonny, jhrte, ji, jib, jiddy, jiff, jiffu, jill, jims, jior, jittering, jittery, jjust, jking,
jkyrjhy, joana, joanna, jobby, jobe, jobh, jobu, jode, jodis, joehoe, joek, joeseph, jogo, johanna, johnathan, johnge, johnieeeee, johnita, johns, joir,
jojn, joked, jokeingi, jokery, jokin, jokwe, jollllyy, jolyy, jondis, jongen, jonhnge, joni, jonny, jonpaul, jonpauls, joo, joooee, jop, jorge, jos,
josephe, joshua, josie, jot, jourd, journalism, journeys, jourplz, jous, joys, jp, jrock, jsem, jsou, jsu, juahida, jubilee, juchu, judo, judy, juego, juer,
juggler, jugglers, juiaiuyto, juic, juices, juicier, juicing, juiice, juist, jujitsu, jules, julie, juliet, julius, jum, jummy, jumper, jumps, jumpsuits,
juneau, jungle, junk, junkie, junkies, junkyar, juno, jurs, jusss, justa, justin, justu, jusy, jut, juts, juz, jveu, jyejtyertuj, kad, kada, kaden, kai, kaija,
kaisa, kake, kalamara, kaleigh, kaliegh, kamaradum, kamikazes, kanackering, kanas, kand, kangaroos, kann, kannst, kannste, kanste, kant, kar,
karats, kari, karl, karlsburg, karma, karol, kart, karte, kartu, kasey, kasse, kasu, katherine, kathiseis, katialuvsasuke, katies, kaula, kavos, kawfee,
kazi, kbye, kc, kcake, kct, kde, kdo, kdyz, ke, kee, keer, kegals, kegger, kehaha, keinen, keister, keith, kej, kekeke, kekekeke, kella, keller, kelley,
kellys, ken, kenners, kenny, kent, kentus, kep, keri, kerritwomey, keshia, keuken, keuze, kevin, kevita, kewleo, kewlest, keybord, keyer, keyers,
keyfood, keystokes, kezboard, kezboards, kezbord, kfklklkkklkllfkllakldfkl, kgfj, kgirl, ki, kichen, kickas, kickin, kicking, kidder, kiddies,
kidnap, kiek, kiera, kies, kiesha, kiest, kig, kijken, kik, killah, killin, kim, kindest, kindi, kindlly, kinf, kingdon, kinkiest, kipz, kirk, kirtinsa, kis,
kish, kisser, kissimme, kisssssss, kissssssssssss, kitc, kitch, kitche, kitchend, kithcen, kitty, kity, kjbitchpoeiuf, kjlkj, kkkkkkkkkkkk, kkty, klamm,
klansman, klanten, klar, kleenex, klepto, klkl, klled, klsduhfiuewgfi, knacerk, knackered, kneel, knit, knkow, knobhead, knocke, knockoff,
knockout, knoledge, knoow, knowin, knowledgable, knowledgably, knowledgeable, knowledged, knowour, knoww, knoz, kocchide, koennte,
koind, kokm, kokot, kolac, kolacek, kolacik, komen, kon, kong, konnichiha, konnichiwa, kontakt, koob, koool, koooool, kop, kopnem, korbania,
korsha, kory, kos, kotaku, kour, kraba, krap, kreme, krieg, kriegen, krijgen, krispy, kristina, krystal, kt, kthx, kthxbye, ku, kua, kuchar, kuchare,
kucharovi, kuchen, kudasai, kudkanker, kudos, kunde, kungfu, kunt, kur, kurt, kut, kutha, kuuut, kuz, kvetina, kwam, kya, kyar, kyara, kyt, laaaa,
laad, laallaaallaaaa, lab, labatt, labotomy, labour, labrador, labrotory, labs, laced, laces, lacht, lackey, lacking, lacqua, ladddyyy, laden, ladey,
ladie, ladila, ladro, ladyboy, ladyman, ladyyy, ladz, lae, laffy, lafite, lagged, laggt, laggz, laguhing, lagune, laidback, laiken, lair, lait, lal,
lalalaaaa, lalalalalalaaa, lalalalallalalalla, lalw, lama, lamborghini, lamo, lampante, lampo, lamps, lampshade, lancashire, landfill, landings, lands,
landscape, lane, lang, langoliers, langosse, languge, lanka, laptopo, lara, laragosta, larddddd, largely, largent, larizona, laroche, lartiste, lasagna,
lasagne, lasanua, lase, lash, lasted, lastest, lastly, latah, latar, lates, latey, lator, latr, latron, lats, latt, latvia, laucnh, laughlin, laughter, laughts,
laugs, launfry, lavandino, lavarmi, lave, lavevo, lavo, lavora, lavoro, lawd, lawer, lawn, lawnmower, lawns, lawrence, layabout, layed, lays,
laziness, lazzy, lb, lbs, lbstr, ldodbdsutoenr, leader, leaders, leading, league, leai, leanr, leans, leaonardo, leap, learner, learnt, leather,
leaveeeeeeeeeeeee, leaveing, leavethe, leavi, leavingnow, leavy, led, leeds, leeeaave, leeftijd, leeks, lees, leeve, leevin, lef, leftin, legally, legit,
leibling, leki, lekkere, lembra, lembro, lemmie, lendburg, lengths, lenny, leonardo, leornardo, lep, lepel, lesbos, lesee, lessee, lesser, lessons,
lestatvalo, lests, letss, lettering, lettucer, letus, letyte, leuk, leuki, leupoloron, levf, leving, levitate, levving, lewis, lewls, ley, lezzer,
lgeipogjpeoahiuwhfoihwofjouwjtpohqeoagjoeirktjpoerjp, lhabitude, lhappen, lheure, liable, liars, liberty, licence, licker, lidi, liebe, lief, liege,
lieu, lieve, lifelike, liffe, lifted, ligaments, liie, liike, liiked, liiks, lijkt, lijst, likable, likeded, likeing, likenectarine, likesir, likesomething,
liketarian, likethis, liketo, likey, likeyou, likie, likke, likkkken, liks, likt, likxe, lilazngirl, liliana, lilke, lillies, lilvi, limbs, limecat, limey, limi,
limitations, limiting, limo, limonate, lindo, linds, lindsey, linear, lined, lineem, ling, linger, lingered, linguistics, linkinpark, links, lins, lint,
lipgloss, lipscomb, lipton, liqour, liqoured, liquir, lisbonne, lisen, listeneing, lister, listing, litehawk, literal, literate, literature, litsten, litt, littel,
littl, litttle, litttlle, liv, livee, livelihood, lively, livers, liyttle, lizard, lizards, lkkr, llamas, llike, llok, llol, llollolo, llooool, lly, lmaf, lmaoooo,
lmaoooooooo, lmay, lnanguages, loader, loaf, loaners, loans, lobb, lobbser, lobbsiez, lobbying, lobcheesecake, lobdter, lobe, lober, loberd, lobert,
lobertsser, lobesters, lobsdter, lobse, lobssters, lobsta, lobstaer, lobsteer, lobstei, lobsterl, lobsterplease, lobsterrrsss, lobsterss, lobstert,
lobsterthermador, lobsterthermata, lobstor, lobstre, lobstser, lobt, loca, locale, locan, locker, locomotive, lodgenet, loek, loft, logo, loin, lois,
loitering, lokaaa, loking, lolaol, lolipop, lollies, lollipop, lollll, lolllll, lollol, lolloloolplololololololololoo, lollovve, lolly, lollypop, lolno, lolok,
lololl, lololo, lolololololololol, lolololoololololoollolol, lolomgwtfbbq, lols, lolzzz, lolzzzz, lon, lond, loner, lonesome, longdrinks, longggg,
longtemp, lonster, loobie, looh, lookd, looke, looken, looker, lookie, lookign, lookink, lookt, loollolo, loololololol, loom, loomk, loompa, looney,
loons, loony, looong, looook, loooool, looooooking, loooooooooot, loooooooser, loooovvved, looove, loopholes, loopt, loopy, loos, looser, loot,
loozer, lopez, lopl, lopster, lords, lorfmaolo, lorie, lorrainetriggs, losbster, loserpant, losta, losterb, losters, lostt, lote, lotion, louisiana, lounge,
lout, louviere, loveley, lovelyt, lovers, lovesome, loveu, lovey, lovin, lovve, lowkey, loyalty, lozer, lp, lpleasurem, lr, lsd, lse, lsober, lsoppy, lst,
ltake, lthe, lthforum, ltike, ltwo, lube, luca, lucci, luch, lucianos, lucious, lucked, luckey, luego, lueish, luerk, luerker, luesers, lueshis, luesville,
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luetang, luie, luigi, luigis, luik, luis, lukt, lul, lullaby, lulu, lululuuu, lumpy, lunchbreak, lunger, lurking, lurrrveeee, lushi, lustig, luvly, luvs, luvy,
luxemburg, lwal, lwoon, lyer, lylat, lynching, lypo, maaan, maaltijd, maamn, maarten, mabe, macaron, maccie, macfilegames, mach, macie,
macies, mack, macnn, macoroni, macupdate, macworld, macy, madagascar, madamoiselle, madan, madddddd, madeira, madian, madison,
madonna, madre, madrid, mae, magarina, magazi, magelion, maggot, maggots, magi, magican, magicians, magicword, magig, magigy, magion,
magna, magnegon, magnet, magnific, magnoin, magrita, mahi, mahm, maia, maid, maids, maike, mailman, maimi, maincourses, maincouse,
mainframe, mainmeal, maintenance, maintenant, maitenance, maitrise, majolini, makaj, makers, makeup, makkelijk, makkelijker, makw,
malahide, malaysia, malbec, malene, males, malolactic, malonioji, malonuma, malvec, mamas, mamber, mame, mameselle, mami, mamm,
mammals, managements, managent, manared, mancia, mandarine, mane, manequin, mang, manga, mange, mangement, manger, mangi,
mangiata, mangiato, mangie, mangina, mangy, manhandled, manhatten, mani, mania, maniara, manic, manim, maninara, manipulatiing, manira,
manjo, manliness, mannequins, mannered, mantain, manual, manydrinks, manz, mao, maplestory, mapping, marachino, maracino, marainara,
marana, maranade, maranaira, maranar, marania, marche, marcus, mare, margaret, margareta, margaritas, margherita, margrita, marguerita,
mariara, marinana, marinare, marinaria, marineri, marinerra, marinna, marino, marinra, marira, marjorie, marlboro, marmelade, marmite, marmyt,
marnianra, marriee, marrinana, martenie, martijn, martin, martina, martine, martines, martinin, martins, martys, marv, marvel, marzipan, mascara,
mascot, mash, mashed, masive, mask, masking, mason, massages, masseuse, mastercard, masterminding, masterpeice, masterpiece, mastro,
masturbated, masturbates, masty, mat, matching, mater, material, mates, matheus, mathew, mathews, maths, matilda, mating, matrix, matto,
maude, maudes, mauled, maurice, mauvaise, mavs, mawy, maximum, maxing, mayans, maybes, maybye, maye, mayhap, mayhaps, mayonaisse,
mayonese, mayor, mayors, mazeltof, mazout, mcat, mccartne, mccartneys, mcchicken, mcd, mcdonlds, mcdonnell, mcds, mchel, mcrib,
mcservers, mcuh, mdame, meabj, meaby, meadium, meager, meaningful, meanl, meano, meanoops, meanu, meany, meare, mearly, measures,
meatbag, meatless, meatpacking, meatspin, meaty, meaybe, meaz, mebu, mechanic, mechanical, meci, medely, media, medician, medicinal,
mediocre, mediu, medley, medoc, medrink, meeeeee, meeeeeeeeee, meejatten, meel, meentime, meep, meetings, mefillet, mega, megabar,
megan, megion, meglio, mehello, mehh, mehr, meike, meisje, mekong, mel, meld, mellisa, melt, mem, memento, memorail, memorial,
memorize, memorized, mena, meneu, menoo, menstruel, mentalist, mentioning, menugion, menukaart, menut, menutje, menuu, menuuu, menyus,
mercury, mercy, merde, meredith, merely, merits, mers, mes, mesasge, mesh, meshi, messaging, messes, messiah, messo, metal, metals,
metaphors, meteen, meteor, methadone, methods, metropolis, mettan, mettre, meue, meun, mevrouw, mew, mewhat, mex, mexic, mexicans, mey,
meybe, mfer, mg, mgnon, mgnt, mhg, mhhh, mhmh, mhmmmm, miam, mican, michel, michele, michelobe, micky, microbrew, microscopic,
microsoft, microwav, microwaved, microwavwe, middlesbrough, midnite, mids, midwest, mie, miestro, mig, migeon, mighon, migidon, miginon,
migione, miglon, mignan, mignet, mignfilet, mignin, mignog, mignoin, mignonj, mignont, migra, migrain, miiine, mijon, mika, mikka, mikkel,
milan, milano, milaty, milck, mild, miljoenste, milkbags, milks, mill, millions, millon, milord, mim, mimik, mimis, mimochodem, minagon,
minamalist, minchia, minchione, minchioneeeee, mindb, mindlessly, mindset, mineeeeee, minefield, mineiro, mineon, minerals, ming, mingalar,
minge, mingeon, mingin, mingion, mingle, mingnet, mingoin, mingong, mingro, mingyawn, minha, miniature, miniclip, miniskirt, miniskirts,
miniute, minnesota, minnon, minnute, minogne, minoqua, minoria, minority, mintime, mintue, minue, minuets, minuit, minuite, minuteds,
minutel, minuter, minuto, minx, miracle, miranera, mircrowave, mirnana, mirtilli, misanderstanding, miscalculation, mishap, misheard,
misinformed, mislabeling, misprint, misschien, misshap, missin, missint, missio, missis, misson, misspelt, missz, mist, mista, mistaking, misto,
mitchell, mite, mitechnology, mitm, mits, mitt, mixing, mixups, mjam, mk, mkjfoiasoigheaobgvidshfoihsdifois, mlady, mlb, mle, mmee,
mmhhhh, mmhmmhmmm, mmir, mmlll, mmmhh, mmmhm, mmmhmm, mmmmflowers, mmmmhm, mmmmmmmmmmm,
mmmmmmmmmmmmm, mmmmmmmmmmmmmm, mmmmmmmmmmmmmmmmm, mmmmmmmmmnmmmmmmmmmmm,
mmmmmmuah, mmmmmuah, mmmmuummm, mmmuuucccchhhh, mmmyep, mmoment, mmore, mmorpgs, mmpfff, mmuahahha, mna,
mnbvcx, mnch, mnd, mne, mnenu, mneu, mneys, mno, mnte, mnuch, mnwhot, moaning, moans, mob, moc, mocking, mod, modeling, modems,
moderately, moderation, modest, modified, modre, mods, modules, moe, moeder, moehaha, moer, moes, moetten, moey, mofuggin, mogul,
moika, moiment, moist, mojitos, mok, mokus, mokysies, mola, mold, moldy, molester, molestes, mollie, mollo, molotov, molp, molta, moma,
momentaraly, momentaril, momentariliy, momenteel, momentito, momentlease, mommah, momment, mommy, mommysh, momnet, momsuck,
monca, monent, moneya, moneyyyyy, moneyz, monez, mongan, mongler, mongnon, mongoalen, moniez, monk, monkeys, monks, monkus,
monment, monoxide, monro, monseiur, monseuir, monsier, monsieurr, monsiur, monsterous, monsuier, montalcino, montana, monte,
montenegro, montgomery, monty, mooched, moods, mooing, mooks, moonlighting, moonshine, moonwalkin, mooooo, moooooo,
mooooooooooooooooooooooooooooooooooooooooooooo, mooraaaa, moose, moosehokie, moovies, mop, moppos, morava, mordoo, morel,
moremoremore, morethan, morgan, morgz, morisse, mornin, moro, morrow, morrowwind, morsel, morta, mortal, mortes, mortgage, mortified,
mortis, morts, moscow, mosey, mosquito, mote, moth, motha, motherf, motherfuck, mothers, motionless, motivates, motivation, motorcycle,
mottle, motto, mou, mouldy, mouni, mour, mourning, mousse, moust, mouthful, mouthfuls, mouths, mouthy, movablegot, movement, mover,
movig, moviment, mow, mozem, mp, mpie, mre, mrnu, mrow, mrrgrs, msy, mtj, muach, muahahahhahahahhahaha, mubbling, mubling, mucha,
muchas, muched, muchlol, muchly, mucho, muck, mucky, mucus, muddy, mudkips, muell, muesli, muggers, mugs, muha, muhah, muhahah,
muhahahahahahaha, muhc, muhh, muir, mula, mule, mulll, mulsim, multi, multilinguistical, multiplayer, multiracial, mumbles, mummy,
munched, munich, murdering, murdoch, musela, musheroom, mushi, mushrooms, musica, musically, musics, muss, musta, mustachio, mutated,
mutch, mutes, muth, mutha, muthafuckas, muuuuuch, muwahahahahahaha, muyst, muzete, muzu, mwa, mwaah, mwaahahahahahaaaa,
mwahaahah, mwahahahahaha, mwahahahahahaaa, mwahahahahahahahaaaa, mwahahahhahahahahahahah, mwuahaha, mya, myabe, myan, myar,
myas, mybe, mybeer, mycousin, mygon, myingin, myl, mylady, mylegs, mylsef, myn, myname, mynes, myo, mysel, myseld, myst, mysterous,
mystery, mystic, mythical, myyy, naa, naaah, naaahh, nach, nachtclub, nactarien, nactarina, nactere, nad, naem, naemlich, nahhh, nahoda,
nahodou, nail, naing, najedol, nakamura, nakid, nakoupil, nameis, namme, namo, nananana, nananananananana, nanananananannanaanna, nancy,
nancys, nane, nanugly, nany, nanyang, naples, napoleon, narinara, nartarine, nasa, nashville, nastyest, nat, nataly, natarine, natasha, natey,
nationality, naum, nauseated, navair, navaril, navel, navigate, navigating, navigator, navy, nazdar, nazev, nbeer, nce, nda, nea, neaaa, nead,
neally, neame, nearsighted, neber, neblogai, necarine, necatraine, neccatrine, necessarily, necessito, necessity, nechcete, necisarily, necitariane,
necks, nectadne, nectaire, nectairne, nectare, nectareen, nectarie, nectarina, nectarite, nectatine, nectaurine, necterarine, nectire, nectirine, nector,
nectraine, nectre, nectrin, nectur, necturine, neder, nederland, neece, needc, needle, needles, needsh, neee, neeed, neeeeed, neemt, neener,
neerlandais, neet, nefink, negotiate, negotiating, negro, negtarine, neh, nehao, neiborhood, neighbors, neil, neiman, neith, nejakyho, nejde,
nejdem, nejdou, nejdrazsi, nella, nello, nema, nemas, nember, nemel, neni, nephew, nephrologist, nephrops, nepisalo, nepojdes, nepotism, nera,
nerar, neratal, nerdd, nerdy, nere, nerf, nergens, nerve, nesideda, ness, nest, nestarine, netarioe, nether, netherland, nethier, nething, netreba, nett,
netter, networking, networks, neucelear, neuh, neuken, neumim, neutral, neutralization, nevada, nevarr, neverming, neverneverland, neverrrrrrrrr,
nevertheless, nevr, nevypinej, newa, newarthill, newater, neway, newbie, newbs, newcomer, newedel, newer, newermind, newfangled, newfound,
newfoundland, newportstyle, newsletter, neww, nextarine, ney, nezer, nezname, neznamyho, ng, ngame, nguyen, nha, nhehe, nhesite, nhoney,
nia, niave, nibbler, nicca, niced, niceee, nicemu, nicew, nicholas, nicht, nicked, nico, nicolas, nicxking, niec, nieco, nig, nigel, nigerian, niggerr,
niggers, nightcap, nightingale, nighttime, nigra, niice, niii, niiiiii, niiiiiiii, nija, nijmegen, nik, niklam, nil, niman, nincompoop, ninfa, nini,
114
ninininii, ninjad, ninteen, ninute, nior, niow, nipping, nipple, nipppels, nirvana, nishe, nitgh, nits, nittygritty, niw, nixon, nju, nknow, nkow, nks,
nky, nlike, nly, nme, nmmmppp, nmmng, nmow, nn, nnnnnnnnnnooooooooooooooooooooo, nnnnnnoooo, nnnnnooooo, nno, nnooooooo, noat,
nobe, noch, noches, nochez, nochn, nodding, noeyes, noit, nojo, nol, nola, nome, nonalholic, noncounter, nonedible, nonesense, nonething,
nonexistant, nonfat, nonfood, nonopium, nonplayer, nonw, noob, noobies, noodless, nooob, nooooooooooo, noooooooooooooooooooooooooo,
noooother, noproblem, nopw, nore, norfolk, norindaland, norite, noriu, norm, normallys, normandy, nors, northampton, northeast, northwest,
norwa, norwegan, norwich, nos, nosh, nosing, nossos, nostalgic, notato, notebook, noted, notepad, noter, notha, nothank, nothingness, nothings,
nothx, noticted, notified, notoriously, notrly, notte, nought, noughty, noughy, noup, nouvellerestaurant, novej, novelty, novo, novy, nowi,
nowpublic, noww, nowww, nowwwww, nowwwwwww, noy, npaid, npc, nromal, nthe, ntry, nub, nucca, nuce, nucka, nuda, nudepants, nuff,
nuffin, nugent, numbered, numbnuys, nun, nurs, nursee, nutin, nutra, nutrastick, nutrients, nutritional, nutritious, nutriy, nutta, nutted, nutters,
nwo, nxt, nya, nyah, nyaum, nyeeeeah, nygh, nyou, nyour, nything, oach, oaf, oakwood, oakyt, oammount, oash, oats, oay, obese, obide,
objecting, objednal, objednam, objetive, objetivos, oblidged, obnoxious, obscuring, observant, obsessed, obsessive, obst, obster, obstruck,
obstructing, obsurd, obtained, obtaining, obvioulsy, ocarbohans, occaisionally, occaisonally, occasion, occasionaly, occhio, occupied, ocd, ocea,
och, ock, ocntests, oct, octogenarian, octopus, oculd, oddest, oddl, ode, odent, oderd, odins, odixe, odne, odoule, odoules, oeat, oei,
oeieoieoieoeioe, oerhaps, ofcorce, ofcoure, ofcuz, ofeer, ofense, offe, offending, offends, offf, offff, offfff, offfffff, offices, official, offord, offr,
offrilo, offs, offtime, offy, ofg, ofme, ofraid, ofrenda, ofri, ofruit, ofsome, oftodays, ofwine, ogden, ogi, ogood, ohayou, ohert, ohgod, ohhch,
ohno, oho, ohok, ohps, oick, oid, oie, oif, oihhh, oiho, oiiiishi, oikok, oin, oink, ois, oit, oive, ojk, okai, okaly, okau, okaydoke, okcobb,
okcoming, okdfsiogjhifdedm, okei, okhun, okido, okiedokie, okj, okkkk, okkkkk, okl, oklahoma, oko, oksir, oksorry, oktea, okthank, okthanks,
okwhatever, olde, oldies, oled, olga, olgas, olhando, olive, olives, olivia, olivis, oll, ollow, olod, olololol, olvido, olympic, omalleys, omelett,
omelette, omellete, omfgwtf, omfgzzzzz, omggg, omgomgomg, omigod, oming, ommmmmmmmm, omnibus, omo, onde, onderstanding, ondrej,
onece, onee, oneee, onglass, onions, onkey, onlyy, onlz, onm, onnnn, onother, onr, ons, ontell, onthave, ontop, ontree, ontslagen, onw, onward,
ony, ooaaar, ooga, oogling, oohh, ooie, ookkkk, ool, oompa, oon, oooaa, ooohh, ooohhhh, oook, oookaay, oookay, oooohhhhh, ooooi, oooookkk,
oooooo, ooooooooh, ooooooooooohhhhh, ooooooooooohhhhhhhhhh, ooooooooooooooooo, ooooops, oooops, ooopsie, ooorrdddeeerr,
ooorrrdddeeerrreeeeddd, ooos, oootay, ooou, oooww, oopa, oopd, oopps, oopsh, oopsies, oopss, oopsys, oor, ootay, oouch, oow, opa, opaddys,
opay, opdrinken, ope, opei, openit, opens, operated, opeten, opff, opgegeten, oph, ophhhh, opinon, opkuisen, opleas, opologies, opp, oppiste,
opportunites, opposed, oppps, oppressing, oppressive, oppure, oprah, oprotten, opsturen, opuie, oput, orabge, orang, orangejuice, orbital, orda,
ordeal, orderand, ordere, orderin, ordert, orderthe, ordo, ordor, ore, organise, organized, orgasmn, orgie, orginally, orgy, oriana, origionally, orite,
ork, orkins, orkut, orlando, oroscope, orphan, orphanage, orphans, orrysorry, orshould, orsomething, ort, osara, oscar, oshkosh, osjdasl, osme,
osmeones, ospatara, osrt, osso, ostrich, otay, oth, othen, othere, otherside, otherw, oto, otoc, otoceni, otpion, ottima, ottimo, ouaais, ouaip, ouais,
ouchhh, ouchy, oue, oughh, oull, oure, ourself, ousp, outaa, outburst, outch, outdiise, outdoor, outers, outi, outisde, outnow, outrages, outsi,
outsie, outsight, outskirts, outso, outsourced, outtttttttt, ov, ova, overdone, overeager, overflowing, overgrown, overhear, overindulged, overjoyed,
overlapping, overload, overloaded, overmind, overpriced, overthrown, overtime, overwelming, ovr, owch, oweell, owen, owes, owl, owners,
owowowowowww, owrk, owrking, owt, owwww, owwwww, owwwwwww, oxbridge, oxycotin, oyheah, oyou, oystah, oyster, oysters, oytu, oyy,
paaaartay, paaayyyyyyyy, paar, paay, pablo, paces, paci, pacino, packages, packed, packet, packing, packs, pacman, padone, padta, pae, pah,
pahtna, painfully, pains, painter, paints, paio, pais, paistaa, paitent, paiud, pak, paker, paki, pakie, palate, palette, palhao, paloozer, palpotine,
palster, palstic, palte, pancake, panele, panic, pann, panrents, pansy, pant, pantsand, paod, papar, papas, papaya, papercut, papers, paperwork,
papi, paprika, parachute, paradigm, paradox, paradoxes, paragraph, paralyzing, paratroopers, paratroor, parcel, pardner, pardoni, parhaps, parish,
parking, parkinsons, parks, parkside, parla, parli, parmazan, parmigian, parodn, paros, parsecs, parsley, partayyy, parte, parti, partial, participate,
particuarly, particularily, particuular, parties, partime, partiuclar, partners, parton, parttime, pascal, pasimatyma, pasiulima, passe, passengers,
passer, passin, passionately, passport, password, pastatysiu, pastatyt, pasto, pasts, pasture, patat, patatoes, patch, pathatic, pathes, patince, pato,
patriotic, patriots, patronize, patronizing, patrons, pats, patst, pattern, patterns, patties, patty, pau, pauline, paulines, pauly, pause, pavement,
pawn, paybthe, paycheck, payes, payi, payig, payinf, paylist, paymano, paymeno, payn, payng, paypal, paypaypay, payu, payyyyy, payyyyyyyyy,
payz, pazazz, pback, pce, pcs, pcworld, peacefull, peacin, peaice, peaople, peaple, pear, pearson, pecials, peddle, pedestrian, pedidos, pediu,
pedophile, pedro, peekabo, peekachu, peeker, peep, peeper, peer, peering, peers, pees, pefect, pega, pegar, peguei, peices, peicre, peiep, peiepie,
peioce, peip, peipeie, peipi, pekna, pence, pencils, pendrez, penetrate, penguin, penie, penislicker, penize, pennis, pens, pensions, peo, peopl,
peor, peple, pepole, peppermint, peppers, peraaaa, perception, perchase, perdect, perdere, perdon, perefct, perefence, perfecte, perfecto,
performer, performing, perge, perguntas, perhaos, perhapse, perhapsn, perignon, perkies, perks, perky, permanent, permette, permetto, permit,
permited, permitted, pernod, pero, perpes, perreo, perscription, perso, personable, personage, personen, personman, perspective, persuasive,
pertelote, pervasive, pervs, pery, pesc, pescadar, pescandar, pesce, pescovegetarian, pesky, pesos, pesticide, peti, petite, peto, petr, petrol, petrus,
petunias, pf, pfffff, pft, ph, phallic, phao, pharmacy, phased, phds, phenomenal, phenominal, philip, philli, phils, phisically, phit, phobic, phoenix,
phonecall, phoned, phones, phonies, photos, photothing, phunkus, phwoar, phx, phycho, physicists, piacciono, piace, piad, picard, picketting,
pickit, pickle, pickled, picknick, picollo, picturing, picutre, piec, piecs, pied, pieeee, pieeeee, pieeeeee, pieeing, piehe, pieiepiepie, pieip, piel,
piemore, pien, piepiep, piepiepie, piepiepiepeiepiepeiepieppiepie, piepiepiepiepi, piepiepiepiepiepiepiepeipie, pieplease, pieppie, piercing, piers,
piesald, pietart, pieu, piew, piey, pieyay, pigeon, pigfucker, pigg, piggsys, piggys, pihb, piie, piiiiieeee, pik, pika, pikey, piks, piles, pilet, piling,
pilipino, pillow, pimmel, pin, pinch, pinching, pineapples, pinkgo, pinkprincesskap, pinkprincesskip, pinotage, pioneers, pip, piper, pipes, pirkeju,
pirrahna, pished, pissflaps, pissup, pist, pitbull, pitie, pits, pittance, pitty, pivk, pixalated, pixel, pizze, pk, pkay, pklease, pklz, plaaat, placeeeeee,
placenta, placing, plae, plaece, plaesure, plague, plais, plak, planing, plannin, plantanos, plassen, plasti, plat, plated, platis, platit, platy, playful,
playinor, playthis, plazure, pleaaase, pleaas, pleade, pleads, pleadse, pleanty, pleasa, pleasae, pleasantville, pleasde, pleasecan, pleasee, pleaseee,
pleaseeeeee, pleaseeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeee, pleasei, pleasemake, pleasesa, pleasese, pleasetake, pleaseure,
pleaseyes, pleass, pleassssssse, pleast, pleasy, pleated, pleeaaasee, pleeaserd, pleeasse, pleeassseee, pleeeasssseee, pleeeeease, pleeeese, plees,
pleese, pleqase, pleqsure, plesase, plesea, pleuren, pleuse, plex, plezz, plike, plish, pllease, plls, plomker, ploy, ploz, plqy, plsae, plssss, plumb,
plumbers, plw, plwaswe, plzzz, plzzzz, pmsl, pnly, pob, pocus, podem, podniku, poei, poet, pogo, pohar, poinsonous, pointed, poisin, poisionous,
poisned, poisnous, poisoin, poiuytreqadfghjkl, poke, pokeeeee, pokey, pol, poland, polen, poli, polic, policeeeeee, policehow, policia, policies,
polievku, polightly, polish, polished, politcal, politest, politically, polled, pollen, pollena, poly, polycarbonate, polyethelyne, polygons, polys,
polytechnic, pomoooooc, ponetchiron, pong, ponkus, ponytail, ponytails, pooba, poodle, pooed, poof, pools, pooo, poooooffffff, pooper, poopie,
pooping, poopoo, poops, poopyhead, poorhouse, pooring, pooty, popcorn, poped, popeye, poppin, popping, poprosim, porb, porblem, porche,
porco, porno, pornography, pornseeker, porra, porsation, porstation, porta, portant, portare, portata, portoguese, portrait, portugeuse, porty, posed,
posion, positions, poslal, posledne, posoible, possesive, possess, possessed, posshible, possib, possiblities, possum, postel, postre, potassium,
potates, potion, potje, potkal, potom, potpie, poubelle, poulet, pourboire, pourions, pourquoi, pourrions, pouting, pouvez, povezd, pow, powder,
powerpoints, powersss, powerweb, pozornost, ppei, pppllleeeaaassseee, ppppppaaaaaayyyyyyyy, praaa, pracitcal, pracktise, practically, praising,
pranked, prasknutia, prat, prate, praten, praticing, pratt, prawn, prawns, prayed, prblem, preapre, preaty, precisely, preconceptions, precooced,
115
predicted, predictions, predinner, preety, pref, prefe, preferance, prefere, prefereably, preferrable, prefessor, pregant, pregnamt, pregnent,
premade, prematurely, premed, premi, premiere, premise, premium, premonition, prend, prende, prendere, prendi, prendre, prends, preoccupare,
prepaird, prepaired, preperation, preps, prerec, preschool, preseant, presense, presently, preso, pression, pressuring, presto, presuming,
presumtiousness, pretand, pretanded, pretended, prettie, prettige, pretttty, prettttyyyy, prettyy, prettz, pretzels, preuni, prevere, prey, prference,
pricl, prievew, prig, prima, primarily, primative, primativo, prime, primerib, primo, primping, prince, princess, princeton, principles, prinest,
printed, printer, printing, printscreen, priorities, priseskite, prisesti, prisoners, pritt, prive, prix, priyesh, priz, prmary, proably, proasta, probalaby,
probaly, probebly, probem, probleem, problema, problemen, problemgrilled, probles, probley, problm, problme, problum, problummmm,
probobly, process, prochaine, prod, profanity, profeessional, profesion, profesional, professionally, proff, proffesional, profi, profit, profits,
profound, profusely, programa, programer, programmers, programmeur, projects, projet, promoting, promptly, pron, prop, propably, propina,
proppe, proprieter, proprietor, proprio, prosche, prosecution, prosesser, prosil, prosim, prost, prostrate, proteins, protocol, prototype, proves,
provides, province, provinces, proving, provocative, provoke, prozac, prpare, prrrrrrrrrrrrttt, prt, prticularly, prwans, prz, psaghetti, psat, psaxno,
psayr, pscyadelic, psecial, pshhh, psicho, psp, pssh, pssht, pssssssss, psssst, psychedelic, psychological, psychologist, psychoward, psycologist,
pt, pty, pubes, publication, publicize, published, publisher, pubs, pucker, pudden, puddin, puddle, pudu, puebla, puer, puff, puffy, puistjes, puled,
puleeese, pulisci, pulse, pult, puma, pumking, pump, pumped, pumpkin, punches, punctual, punds, punished, puo, puodeli, pupon, puppies,
purchased, purchasing, purest, purgatory, purhapse, purposes, purr, purrrrrr, purrrrrrr, purses, purto, pushed, pussies, putang, putaway, puted,
puter, putero, puting, putnam, puto, putrescent, puttng, putu, puynch, puzzle, puzzles, pwc, pwease, pweaseee, pwitty, pwn, pwnt, pwnzer, pws,
pyan, pye, pylons, pyper, pyramidal, python, pzl, qaeda, qaint, qall, qbout, qero, qfilet, qhat, qiocl, qld, qnat, qnd, qntos, qny, qondring, qq, qre,
qs, quaint, qual, qualcosa, qualcosaltro, qualifications, qualify, qualitys, quand, quant, quantity, quantum, quare, quase, quck, quebequois,
queestce, quelle, quello, quenches, quero, querty, query, queso, questi, questing, questio, questionable, questionasker, questioneir, questionmark,
questionmarks, questionnaire, questo, quete, queuing, quicjk, quickest, quickily, quickliest, quicklu, quickr, quid, quiiiiite, quik, quiles, quinch,
quindi, quirky, quita, quiting, quits, quizzing, quoi, quoiiiiiiii, quotes, qwd, qwee, qwell, qwerty, qwertyyuiopasdfghjklzxcvbnm, raa, raaaagh,
raaawer, raad, raawrrr, rabbi, rache, racism, radar, rader, radiant, radiation, radish, radley, raedy, raelly, raep, raeuber, rafters, rafting, rage,
raging, raid, raiding, railroad, rails, rainbow, rainin, raison, rake, rally, ramen, randier, rang, ranged, ranges, ranked, ranking, ranks, ransacked,
rant, rapacity, rapeing, raperaperape, rapes, rapide, rapidly, rapper, rapture, rarely, rares, raspberries, raspberry, rasperry, rasta, rasturant, rata,
rated, ratesa, raus, rave, ravenous, ravens, raving, ravinous, raviolis, ravishing, ravonusly, rawks, rawrrr, rawrrrr, raz, rb, rbeer, rbis, rde, rds,
rdsgbdfg, reaaady, reaaly, react, readdy, reader, readey, readin, reads, readty, readu, readyt, readyyy, reafy, realises, realistic, reallity, reallllyy,
reallly, reallyd, reallyt, realname, realt, reapet, reare, rearranging, reasaurants, reasdy, reasl, reasonably, reasoning, reassignment, reastaurant,
reasturant, reat, reataurant, reattach, reay, rebecca, rebuild, rec, recalibrate, reccomme, reccomned, receipts, received, recemond, reception,
receptionist, receptor, recet, rechung, recieved, recimend, recipie, recipt, reco, recogmend, recolor, recom, recomanded, recomd, recomed,
recomendatio, recomendation, recommand, recommedation, recommei, recommemend, recommen, recommendatio, recommendationsz,
recommende, recommending, recommends, reconize, reconmend, recoomend, recording, records, rectangle, recycling, redcoat, reddy,
redecorating, redhead, redheaded, rediculo, redneck, redone, redw, redwhite, redwin, redwood, redwould, ree, reeaaal, reek, reet, referred, refesh,
reffill, refiil, refiill, refile, refl, reflection, reflex, refresher, refri, refrigerator, refwsfsddxsgfdsgfdsgfdgddfdgddfdfdffddddfdfgfddff, regain,
regestier, regimen, reginaaaa, reging, regional, regions, regisetr, registe, registered, registor, registra, registrar, regit, reguest, reguister, regulary,
regurgitation, regustir, rei, reimburse, reimubrse, rejected, rejoin, reknes, rekonmend, relating, relations, relaxes, release, reliever, religious,
relized, relly, relocate, relocating, remade, remainder, remaining, remake, remarkable, remarked, remarks, rembermeu, remedied, remedy,
rememberd, remembr, rememcer, rememebr, remenber, remercie, reminding, remindng, remmember, remobe, removed, remover, removing,
remplace, renderings, rends, rene, renember, renewed, reno, renound, renovate, renovatie, renowned, rensalaer, rensselaer, reommend, reomve,
repairs, repared, repeating, repellent, repented, rephrase, replate, replaying, replete, repopulate, reports, representaitve, reprimand, rept,
republican, reqady, requests, requirement, requires, requirment, resarant, resart, resather, rescue, rescued, researched, researcher, researchers,
resemble, resembles, reset, resevations, residential, resign, resisted, resoanes, resolution, resonant, resources, respecter, respective, respects,
respiratory, responds, responses, responsibe, responsibility, resrerch, restarants, restararnt, restarau, restaraunts, restarting, restarunt, restaunt,
restauranbt, restaurante, restauranteur, restaurantpleas, restauranty, restaurat, restauratn, restauraunt, restaurent, restaurnant, restaurnat, reste,
resterant, resteraunts, resterraunt, resting, resto, restorant, restore, restourant, restraining, restraurants, restrawnt, restrictive, restront, rests, restu,
restuarnat, restuff, resturants, resturarnt, resturunt, restwrant, results, resurrected, retaraining, retire, retired, retrive, retuarant, retur, returne,
returnm, reujreth, reuse, reuze, rev, revealed, revenge, reverse, reviewd, reviewer, reviews, revised, revising, revive, revolting, reward, rewarding,
rexlaxing, reyes, reyou, rfill, rfser, rgh, rgiht, rgr, rh, rhank, rheres, rhine, rhode, rhone, rhum, rhythm, rib, ribbs, ribeye, rican, ricans, richmond,
richt, ricordati, ridance, rider, rides, ridiculess, ridiculos, ridin, ridins, rids, rie, riesce, rife, rifle, rig, rigamortis, rigggghhhhhttttt, rigght, righgt,
righht, righhtt, righta, rightaway, rightback, rightclick, righy, rigor, rigt, rigthh, riiiiiiiiight, rill, rimmed, rimmer, rine, ringin, rio, riot, riots,
riperete, rips, riscuoti, rising, risotto, risque, rist, ristorante, ritchie, ritualistic, rivederla, riverfront, rj, rm, rme, roaches, roadkill, roads, roam,
roaming, robbbery, robben, robbers, robbign, robbin, robby, robbys, robe, roberry, roberta, roberto, roberts, robery, robins, robo, robotic, roboto,
rockabilly, rockafellah, rockateer, rocky, rockys, rod, rode, rodents, rodeo, rodney, roffffl, roffle, roflaarp, rog, roid, roide, roight, rol, rolaids,
roleplaying, rolf, rolllll, rollocks, roma, rome, romeo, romm, romolo, romp, rompere, ron, ronaldinhos, ronalds, ronni, ronnie, ronud, roo, rood,
rooftop, rools, roomie, roomy, roosevelt, ropes, rored, rosie, rosspaulcurtis, rot, rotflmao, rothschild, rott, rotted, router, rowr, rox, roxxored, roy,
royalty, rozemarijn, rpi, rplate, rrelu, rright, rrr, rrraaaaaarrrrhrhhhhhghggggggweogk, rrrr, rrrriiiiiicccccchhhhh, rs, rsk, rsoup, rsrsrs, rsrsrsrrsrs,
rsrsrsrsrsrsrrsrs, rt, rte, rteuyrte, rteyh, rtong, rubarb, rubbed, rubish, rubs, ruddy, rudely, rue, ruffian, rugged, rught, ruimte, ruins, rumania,
rumble, rumbling, rumdrehen, rumor, rumors, rump, runaround, runawayyyyy, runcible, runed, rung, runing, runnaway, runnnnnn, runny,
runway, rupee, rupeeeee, rupert, rural, rushin, rusi, russhing, russihs, russle, rustles, ruth, ruud, ruuun, ruuuuuuun, rwarrrrrrr, rwe, rwy, ryeu,
sabbath, sabe, sabiina, sabiinah, sabina, sabirina, sachs, sacrafices, sacrifices, sadaj, sadder, sadists, sadnem, sadnite, sadnut, sado, sadoler, saed,
safer, safeway, safty, saggy, saghetti, sahahahhaha, saharis, sahll, sai, sailors, saink, sais, sait, sak, saked, salaade, saladand, saladcomeing,
saladdddd, saladfor, saladpz, saladspaghetti, salami, salamn, salamo, salamoon, salan, saland, salde, salds, sall, sallery, salman, salmion, salmoin,
salmond, salmone, salmonorsomethingels, salmonplease, salmony, salomn, salon, salsd, saltier, salts, saluee, saluete, saluto, salve, samba, samen,
samer, sammidges, sammon, sammys, samol, samoln, samons, sampler, samples, sampling, samthing, sand, sandrler, sanduich, sanduwich,
sandwish, sandwitch, sandy, sane, sang, sangria, sanitary, sanitation, sankyu, sans, sant, sante, sao, saomon, saoup, sap, sappire, sappy, saprano,
saranno, sarasota, sarry, sas, sashays, sate, satellite, satiety, satifactory, satified, satifies, satisfy, satisy, satrt, saty, saucer, sauerkraut, saufen,
saulmon, saunters, saus, sausages, sautee, sav, savage, savings, savory, sayar, sayd, sayed, sbagliato, sbobba, scab, scalded, scammed, scammer,
scamming, scamp, scampi, scappo, scar, scarey, scarpe, scedual, scenary, scenic, scents, schat, schedual, scheduled, scheisse, scheme, schenay,
scherzo, schhool, schifo, schijnt, schiool, schleich, schluck, schmancy, schmo, schmules, schnickschnack, schnityel, schnitzel, schnockered,
scholar, scholardships, scholarship, scholarships, scholl, schreewem, schucks, schum, schwaechlinge, schweety, scince, scold, scoming, scone,
scooby, scoops, scope, scorcessi, scores, scot, scott, scoundrel, scoundrels, scouse, scousers, scrabble, scram, scranton, scratches, screamer, scren,
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screwdrivers, screwing, scribbles, scribbling, scriptures, scrolls, scrolosis, scrounge, scrumptous, scrumptuous, scuba, scum, scumbag, scusa,
scusse, scuz, sdalad, sdfasdf, sdfds, sdfksahdfgsahdgkjtartjsdfhprty, sdfmmdbfjsdbgf, sdh, sdit, sdoty, seaat, seabass, seafoods, seal, seam, sear,
seasoning, seasted, seate, seater, seatings, seatp, seave, seayt, sebastien, sebastine, secinds, secksy, secluded, seco, seconda, secondd, secondo,
seconed, seconf, secretly, secured, securitz, seeay, seebye, seed, seeds, seeee, seeeeeeeeeeeeeee, seemedlike, seeming, seemingly, sees, seeyou,
sef, segni, segundo, sehk, sehr, seile, seing, seintje, seiously, seirously, seitan, seizures, seizuring, sek, sekf, sekrets, sekrits, sel, selecting,
selections, selflessness, selfserve, seller, sells, selmas, selmon, sem, seman, semantics, sembra, semems, sence, sending, seniora, seniors, senn,
sensations, sensative, sensativity, sensibly, sensitve, sensory, sensual, sente, senti, senurita, senza, separate, seperatly, sera, serene, serever,
serieus, seriosly, seriouslly, serive, serivice, sertinly, serv, servatude, servay, servce, serveren, servesa, servic, serviced, servie, servings, servis,
servise, servo, servvice, sesame, seskite, seteak, setting, seulement, seve, seved, severance, sevrices, sew, sewer, sewercide, sexay, sexcellent,
sexi, sexier, sexing, sexistic, sexually, sexuel, sexylove, seya, seymore, seymour, sf, sfawrtewqrtewfmhasdf, sfhd, sfhj, sfj, sgood, shadow,
shagetti, shaky, shalt, shameful, shampagne, shandy, shangri, shanty, shao, shaquisha, shaquishas, shar, shareefa, shark, sharpei, sharper, shat,
shaushuahsa, shawty, shay, sheckshun, shedding, shee, sheeoot, sheepbased, sheese, sheets, shef, sheila, shelby, shelock, shepherd, sheriff,
sherrif, sherry, shet, shewd, shh, shhh, shhhh, shhhhh, shhhhhh, shhhhhhh, shhhhsh, shhht, shibbity, shiet, shifinshed, shifty, shiloh, shimp, shin,
shining, shinn, shins, shiny, ships, shir, shirley, shirly, shirmp, shirty, shitcanning, shitcock, shite, shiteating, shitface, shiting, shitload,
shitmuffins, shitplace, shitshit, shitter, shittt, shitttt, shitwit, shiver, shivering, shizzle, shl, shld, shmegal, shocked, shoddy, shoiodnt, shon,
shoneys, shood, shoooeeeebyyy, shoor, shootem, shooter, shooting, shoots, shopin, shoplift, shores, shorstaffed, shorted, shortest, shorthand,
shortie, shortl, shortline, shortstaffed, shoudl, shoul, shouldent, shouldhuh, shouldn, shouldnjt, shouldve, shound, shoved, shovel, shoves,
showering, shpeshul, shpuld, shrieking, shriveled, shroder, shropshire, shshshouldaa, shtharp, shuda, shuffle, shugar, shugs, shum, shuold, shup,
shuuurreeee, shuv, sia, siagon, siamese, sibling, sibs, sic, sicily, sicken, sickkk, sickness, sicko, sid, siding, sie, sieda, siediti, siee, sieg, siehst,
siento, sieve, sighting, sights, sightseeing, signal, signed, significance, signifying, signior, signor, signorina, signour, signs, sii, siling, silliness,
silllllyyy, sillly, silvio, sily, simici, simpathize, simpl, simpleton, simulated, simulation, simulcra, sinagpore, sincere, sincerely, sincerest, sind,
sindy, singaporean, singin, sings, sinkd, sinkin, sinta, sinuey, sioban, siobhan, siohban, sioux, sipo, sipping, sippy, sips, sipz, sirah, sirens, sirr,
sirrr, sirrrr, sirus, sirwould, siryou, sissy, sita, sitd, sites, sith, sititng, sitpat, sittadwn, sitted, sittt, sittting, sitx, sixo, siye, sizors, sizzurp, sjc, sjg,
sjht, sjould, skanaus, skanios, skate, skaters, skauda, skdksl, skeeeeeet, skeezebag, sketti, skiddy, skies, skiiiiiin, skillfully, skimpy, skinned,
skinnier, skins, skipping, skl, skull, skunk, skward, skwl, skyhigh, skype, skyrocketing, sl, sla, slaad, slab, slacker, slacking, sladad, slaed, slainte,
slake, slammer, slang, slanty, slape, slapped, slash, slav, slavery, slay, sleazy, slecht, slechte, slection, sleeper, slicing, slides, sligl, slime, slimey,
slippes, sliurpiu, slive, slob, slobber, slobbered, slobt, sloew, sloow, slop, slowest, slowness, slowy, slu, slum, slurring, slurrp, sluthead, sluts,
slutty, sly, sm, smaakt, smackerel, smacks, smalll, smalls, smalltown, smamich, smartass, smarter, smartest, smartnes, smarts, smearing, smeg,
smel, smelled, smellsterrible, smelss, smelt, smething, smiley, smily, smine, smirk, smithereens, smithes, smithtown, smits, smittey, smitty,
smoked, smokes, smokey, smoothier, smothly, smting, smushing, smw, smwhere, snacking, snacks, snadiwches, snadwiches, snag, snapperhead,
snaps, snatched, snatching, snatchy, snazzy, snd, snded, sneakery, sneeky, snicker, sniffles, snippy, snist, sno, snock, snockered, snore, snorting,
snowed, snowy, snrta, snug, soaked, soare, sobered, sobr, soccerfan, sociable, socialize, socially, sockeye, sodawater, sodz, soecials, soemoen,
soemthign, soemthing, soend, soeone, soepe, soething, soetmhig, sofa, sofia, softdrink, softdrinks, softy, soi, soiled, soiperb, soir, soiree, soiup,
sojaballs, sokay, soke, sol, solad, solder, solderin, soldering, soldi, sole, solidly, solitude, solution, somat, somats, sombody, somebodys,
somedishes, somefilet, somehtign, somehtng, somehwere, someing, somenting, someoe, someon, somepie, somerue, somes, somet, somethigto,
somethihg, somethimes, somethimg, somethine, somethings, somethinh, somethint, somethnig, someti, someties, sometihin, sometihng, sometme,
someway, somewehere, somewere, somewhre, somewine, somking, somme, somnething, somone, somos, somr, somtime, somw, somwhere,
sonic, sonny, sonofabitch, sooc, soom, soomn, soonishness, soonski, soooome, soooon, sooooooo, soooooooo, sooooorrrrrryyyyy, soooup,
soorrry, soorry, soorz, soothing, sopa, sophisiticated, sophisticated, sophmore, soposse, sordo, sorii, sorrrryyy, sorrry, sorrryt, sorrt, sorru,
sorryabout, sorryanything, sorrylol, sorrysir, sorted, sorting, sorwhat, sot, sotaly, sothats, sotty, soub, soud, soudns, souds, souep, souip, souldnt,
souleating, souls, soulstealer, soundsgreat, souop, soupde, soupdu, soupdujour, souper, soupes, soupl, soupnpie, soupo, soupp, soupr, soupsalad,
soupsit, sourced, soutehr, southampton, southcoast, southeast, southest, souvenier, souvenirs, soviel, soylent, spacecake, spaces, spacials,
spaciels, spaded, spaegetti, spagatie, spagatiie, spagehhti, spagehttie, spageiti, spagethhi, spagethii, spagetiit, spagetiti, spagetiy, spagett,
spagettic, spagettti, spagettu, spagetty, spagety, spagfhetti, spaggetie, spaggetii, spaggett, spaggettiiiiiii, spaggettti, spaggetty, spaggety, spaggheit,
spaggitti, spagh, spaghaitt, spaghatti, spaghei, spaghetta, spaghettia, spaghettig, spaghettiplease, spaghetts, spaghetty, spaghitte, spaghrtti, spaght,
spaghti, spaghttei, spaghttii, spaghyes, spagrty, spagti, spagtiit, spagtthi, spagtti, spagueti, spaguettie, spahetgettie, spahgitti, spams, spangetti,
spanghetti, spaniard, spanich, spanis, spanishman, spargueti, sparicus, sparing, spark, sparkly, sparky, sparsely, spartaaaaa, spartaaaaaa,
spartaaaaaaaaaaaaaaaaaaaaaaaa, spasm, spastic, spathetti, spawn, spawns, spays, spaz, speacl, spead, speakig, speakin, speakkk, speaky, spears,
specai, spechal, specia, speciald, specialitis, specialitu, specialitz, specialization, speciallite, speciallity, specialthough, specialy, specifics,
specified, specify, speck, specl, spectals, speeding, speeds, speeeewwww, speelde, spegeati, spegette, spegetthi, speggeti, spegghetti, speghatti,
spegheti, speghetta, speghitti, spegtii, spegttii, speical, spek, speke, spekked, spelen, spelt, spends, spendy, sper, spermador, speshully, spgetti,
spghatii, spghatti, spghti, sphegetti, sphegettie, sphgehetti, sphgetti, sphgksdkteasdflkjz, sphhagetti, spiders, spieces, spiel, spiffin, spiffing,
spigheti, spike, spil, spiled, spilt, spine, spinnaz, spinng, spinnig, spinnin, spinny, spiritual, spiritually, spirt, spite, spits, splains, splat, splintercell,
spnning, spo, spoiled, spoilers, spoiling, spoleto, sponge, spongebob, sponsor, spontaneously, sporak, sporchi, sportssuit, sportswear, sposet,
sposta, sposted, spotless, spotty, sppaghetti, spr, sprache, sprachs, sprays, spreak, spreche, sprechers, springs, sprinter, sprit, sprlled, sprry, spu,
spuds, spugetti, spuitje, spun, spunds, spunkie, spup, spus, sqare, sqaure, sqeezez, sqeezing, sqiurms, sqlqd, squa, squad, squares, squarre,
squashed, squat, squatz, squeezing, squid, squire, squirrel, squirrels, squirty, squish, squishy, squre, srevice, sri, sright, srorry, srsly, srz, ss, sseat,
ssert, ssex, ssir, sso, sssooo, ssssheat, ssssorryyy, sssssir, sssssssss, sstart, sstop, sstory, staaaarving, staaarrsss, staan, stabbed, stabby, stackable,
stacy, stacys, staf, staffed, stagg, stain, stairing, stairs, stak, stakin, stako, stalkers, stalling, stallions, stamped, stanca, stange, stank, staph,
starcraft, stards, stared, stareing, stareout, stares, starrt, startersplz, startes, startin, startving, starway, starwipe, stashed, statasfying, statau, stater,
stating, stationery, stato, statue, stature, status, stavamo, stayed, stayonline, staz, stck, std, steack, steadward, steaj, steakk, steakkk, steakkkk,
steakl, steaksouse, steals, steaming, steamy, steano, stear, steask, steck, steer, stefani, stell, stem, stenders, stepfathers, stephanie, stepping, steppp,
steps, stereotype, stereotypes, stereotypical, sterile, stern, sterter, stevage, stevenage, stevens, steves, stevie, stevo, stff, sthanks, sthe, sthir, sti,
stickem, sticker, stickler, stickyness, stif, stiffing, stijn, stil, stilling, stilworks, stinkier, stinkiest, stitch, stitched, stitches, stition, stnading,
stoamach, stocked, stockroom, stockton, stockyards, stoff, stoic, stoj, stolid, stomac, stomachache, stomachs, stomme, stomp, stoole, stooolen,
stopppp, storage, stouter, str, straat, strabge, strada, strafe, straifgt, straigh, straightaway, straing, straining, stranded, strang, strangled, stranglely,
strangley, strangly, strap, straps, strat, straws, stray, stre, streaching, streatch, streching, stregnth, strenuous, stressed, stressing, stretching, stricly,
stright, string, stringfellow, strings, striped, stripey, stripped, strokeable, stroking, stron, strongly, strreet, strt, structral, structure, structures, sttet,
stubborn, stucj, studiando, studio, studly, studnt, studys, stuen, stufff, stuffing, stuipd, stuk, stukken, stull, stumbles, stumbleupon, stumped,
stunk, stupendous, stupi, sturdy, sturen, stutter, stuttering, stuttgart, stuudent, stuvk, stuyin, styles, stylish, styx, su, sua, subito, subjective, subs,
117
subsatisfactory, subscription, subsidies, subsidize, subsidizing, subsitute, substance, substances, substantiated, subtleties, subtlety, suc, suca,
sucaaaaa, succeeded, succulenting, sucess, suckeeer, suckin, suckk, sucre, suculent, sudenly, sudoku, sued, suerte, sues, sufen, suffisament, sug,
suga, sugarpie, sugetst, sugettcst, sugg, suggere, suggestive, suggs, suggst, sugs, suince, suite, suivre, suker, sullalbero, sully, summeyt, summon,
summt, sumo, sumol, sumting, sumwhere, sunbeam, sunda, sundia, sunflowers, sunk, sunless, sunrise, sunset, suonare, suoup, supa, superglued,
superhero, superheroes, superior, supermarche, supersizedthree, suplot, suposed, suposs, suppa, supplier, supply, supportive, supposrt, supreme,
suprisingly, suprize, suprr, supu, surcharge, surel, surem, suren, sureply, suret, surewater, surfers, surger, surplus, surprins, surprises, surr,
surreality, surrender, surrey, surroundings, surrreeee, surry, surv, surve, survice, survive, survived, surviver, survivor, survy, susans, suspended,
suspense, suspicion, suspiciously, sustanence, sustenance, suth, sutinku, sutra, suugets, suuupppperrr, suvey, suvgiau, suwattenisai, suzanna,
suzanne, svelt, sventlana, svrwxdata, swadadafdsgf, swallow, swallowing, swallows, swam, swapped, swaying, swayzes, swbf, swears, sweater,
sweaters, sweatheart, sweats, swedish, swee, sweel, sweetcakes, sweeter, sweethaert, sweetheard, sweetly, sweetthang, sweetums, sweetz,
swelling, sweping, swept, swet, swettie, swich, swicth, swig, swill, swinepigs, swingers, swinging, swirl, switcheroo, switchin, switching,
swityerland, swivels, swollen, swoon, swordfighting, swordfish, swould, swtich, swung, swwweeett, sxalmone, sxc, sxcschs, sxy, sylvainstephen,
sylvianstephen, symbol, symbolic, symbols, sympa, synergy, synthetically, syphilis, syre, syrene, syyyr, syyyyyyr, sz, szeah, taaaaa, taagen, taak,
taarten, taartspel, taary, tabble, tabke, tabkle, tablecloth, tableclothes, tableconnected, tabledance, tablee, tablets, tableware, tablke, tablw, tabul,
tabuli, tadam, taee, taew, taffy, tags, tahank, tahks, tahnx, tahoe, tahts, tai, tails, taime, tainted, tairn, takeaway, takein, takethis, takev, takn, takr,
taky, tal, talamoon, talb, talbe, talble, talkitive, talkng, talks, tallenting, tallest, tallk, tallyho, tam, tambem, tampa, tan, tand, tankas, tankeyo, tanls,
tanx, tapeworm, tapporte, taps, taqke, taqueria, tarantino, tardiness, tardy, tarete, target, tarnation, tarquin, tarr, tarrt, tarsan, tartare, tarth, tartlet,
tarty, tary, tarzan, tase, tasks, tasky, tassoiur, tastee, tastefull, taster, tastesd, tastiest, tastin, tastyas, tasy, tataedrfhasijfge, tatea, tater, taters, tatoo,
tator, tattoos, tau, tavle, taw, taxes, taxis, tay, taylor, tbale, tbeef, tbh, tboo, tbs, tbser, tbut, tch, tchau, td, tday, tdont, teaa, teaaaaaaa, teabag,
teachings, teacup, tead, teah, teak, teaplease, teaq, tearful, teart, teash, teasing, teatea, teatt, teaz, teba, tec, techincal, technica, techniques,
tecnology, ted, teda, tedka, teds, tedy, teea, teeheee, teeming, teen, teenage, tees, teet, teetje, teetotal, tegen, tehehe, tehehehe, tehhe, tehre, tehres,
tej, teka, teke, teks, tela, telefono, telekenesis, teleportation, teleported, teleporting, telescope, telescopes, telipathic, tellow, telly, telp,
temperature, tempereatur, tempo, temporarily, tempranillo, tenacious, tendacy, tendar, tendeer, tenderized, tenderly, tending, tengo, tenn,
tennants, tennis, tension, teo, tepid, tequilla, tequitos, tequla, ter, terecht, terible, teriner, termador, termator, termina, terminal, terminez, terms,
terra, terrably, terrabol, terrace, terrains, terrier, terrine, terrist, territory, terrivle, terroire, terrorist, terrorits, tesco, tessa, testa, testament, tested,
tests, teth, tetley, teun, tevening, tevin, tewel, texll, texs, textanything, textbeeer, textberry, textbooks, textbye, textcan, textcherry, textcherrypie,
textcobb, textcoffee, textcoffie, textfilet, texthi, textim, texting, textlets, textok, textsir, textsit, textsure, texttable, texttea, textthabk, textthank,
textthanks, textthats, textwhite, textyes, textyum, tez, teza, tf, tghank, tgif, tgirl, tgo, tgrjhyt, thaaaaanks, thabkyou, thaha, thai, thakn, thakns,
thamedo, thamk, thanbk, thanck, thanhk, thanik, thanka, thankd, thankee, thankeee, thankgs, thankj, thankjs, thankk, thankkyou, thankl,
thankqwe, thanksa, thanksgiving, thanksgood, thankshehe, thankshh, thanksm, thanksss, thanksssh, thankssss, thanksth, thanksu, thankyoooo,
thankyoumiss, thankyouno, thankyouu, thanmks, thannks, thanxs, thanz, thas, thash, thass, thast, thata, thath, thathanks, thatjumpsuit, thatl,
thatsa, thatsd, thatsir, thatsok, thatsoup, thatt, thatwe, thatwould, thaty, thatyou, thaught, thay, thaznk, thbk, thc, thead, theanks, theare, thease,
theater, theather, theatre, thebaby, thebar, thebill, thec, thecheck, thechef, theer, theere, thef, thegrilled, theh, theing, theirse, thel, themdaor,
theme, themenu, themo, themovie, thena, thence, thenew, thenm, thenn, theobjective, theod, theodore, theories, thep, thepasta, thepatio, therapist,
therapy, thereckoning, thereckoningband, thered, theree, therell, therepie, theright, thermadoor, thermardo, thermarmor, thermdaor, thermindor,
therminore, theroad, therse, thes, thesalad, theses, thesisi, thespaghetti, thespecial, thespecials, thethe, theveg, thevege, thewine, theworld,
theyellow, theymgiht, thhanks, thhmm, thibault, thid, thieves, thifs, thig, thign, thiholy, thiiiing, thiiirstyyyyy, thiink, thikn, thiknk, thine, thinge,
thinging, thingsre, thingyouve, thingyy, thinkingnow, thinkj, thinkkkkkk, thinkyou, thiopia, thiought, thirstie, thirtysomething, thisand,
thisevening, thisgame, thismeal, thismishap, thissy, thiswhole, thius, thjanks, thje, thjis, thkanks, thm, thnax, thne, thnka, thnkz, thnkzz, thogt,
thogu, thomas, thomaz, thompson, thoo, thoose, thoroughly, thosethis, thou, thoughtfu, thoughti, thoughtrs, thour, thousands, thouse, thprogram,
thpught, thqt, thrador, thread, threadwin, threapists, threatening, threes, threesomes, thrice, throug, thrown, thrres, thruhis, thrym, thsanks, thsnak,
thsnk, thsnks, thso, thsts, thsuit, thsuppose, thta, thu, thunderbird, thunderbolts, thunderstorm, thunderwear, thur, thursy, thwife, thxbabe, thxs,
thy, thyis, thz, tiavril, tickets, ticking, tickle, tickled, tickt, tiddying, tido, tidying, tied, tienes, ties, tiete, tieten, tifa, tiffany, tiffeny, tig, tigers,
tightass, tihiii, tihis, tihistry, tihnk, tiime, tiip, tijuana, tik, tikejausi, tikra, tile, timbers, timedifference, timeeeee, timer, timewaster, timezones,
timies, timing, tims, tina, tinha, tinkin, tinme, tiolet, tipicalintei, tippped, tipss, tipst, tipycalinternetquestion, tiring, tisch, tisk, tissue, titans, tities,
title, tje, tk, tka, tkae, tkekekeke, tlak, tlets, tlooks, tloolz, tlulz, tme, tmuuahahhahah, tmy, tn, tnak, tng, tno, tnoight, tnow, toastman, toasty,
toasy, toat, tober, tock, tod, todat, todaysorry, todayt, todayy, todayyyyiee, todaz, todd, toddays, toddler, todinner, todsays, toetje, toevallig, tofor,
tofusalmon, togather, toget, togetha, toghter, togo, tohave, tohear, toilette, toilettes, toked, toking, toldme, tolds, tolit, tomake, tomarrow, tomates,
tomatoe, tomatos, tome, tomeivens, tomfoolery, tommoorow, tommorw, tommrow, tomo, tomorro, tomorrw, tomos, ton, tonal, tong, tonggg,
tonighthoney, tonit, tonya, tonyyyy, toolkit, toomany, toooooo, toooosoo, toorder, toot, toothpick, tootsie, topic, topics, topless, topps, tops,
topup, topytryutuoyto, tor, torino, torn, tornero, torte, tortured, tos, tosser, tost, tostitos, totake, totall, totaller, totaly, tote, tothe, tottie, touches,
touchet, touchpad, touchy, tougher, tought, toujours, toung, tounges, toungh, toupee, tourbooks, tourettes, touring, tourism, touristy, tourney, tous,
towed, towels, towers, towners, townnnn, towns, toyou, toys, tphank, tplease, tpo, tpresto, tra, trabalho, traci, tracking, tracks, tracksuits, tractor,
tracy, tradefair, trades, tradional, traeting, traffics, tragically, trahs, traigo, trailed, trailor, trake, transaction, transfer, transferred, transgender,
transgendered, translates, translation, transparant, transported, transvestite, transvestitie, transylvania, tranvestite, trappe, trashcanman, trashed,
trashy, trauling, traumatic, travell, traveller, travels, trboule, trcuker, tre, trea, treasury, treatcherous, treatments, treats, treba, treck, tred,
treeborrrr, treeeee, treeeeeeeee, trench, trentino, tress, treti, trews, tri, trial, triathlon, triathlons, tribbin, tricked, tricky, tright, trillion, trim, trin,
tring, trinity, trink, trippin, tripple, trips, tristate, trk, troblem, trocar, troia, troisrivieres, trol, troll, trollin, trop, tropic, trots, trough, troup,
trousers, trouver, trovi, trow, troy, trtying, truc, truckers, trudy, truelly, truffle, trumps, trusted, trusting, trusts, truthful, tryand, tryed, tryit, tryng,
trzit, tsake, tshall, tshirts, tsos, tss, tsure, ttake, ttfn, tthanks, tthat, tthats, tthere, tthhaatt, tthts, ttime, ttimes, ttle, ttomorrow, ttteeeennn, ttwo, tty,
tub, tubby, tucson, tudo, tuely, tuen, tuesdays, tugging, tuggy, tuhle, tulip, tum, tumble, tummys, tun, tungsten, turbokinny, turckey, turds, turista,
turkeys, turkish, turquoise, turtlepower, turtorial, tuscan, tuscon, tush, tutirla, tutoial, tutoral, tuts, tutto, tuturial, tuurlijk, tw, twait, twank, twats,
twatty, tway, twee, tweke, tweleve, twenties, twentyfour, twentys, twentytwo, twentz, twhat, twhere, twilitgth, twinings, twins, twirling, tword,
txd, tyalking, tyea, tyep, tyes, tyhe, tyme, tyou, tyought, tyourself, typeface, typicall, typisch, tyr, tyrant, tyumm, tyyou, tz, tzo, ua, uahauhauhaua,
uahsuahsuhauhsa, uahuahauhauha, uahuhauhauhauhauhauahuahauhauhauahua, uaieaueahieuaheiahieha, uany, uare, uarrrhrhrhhrhhrr,
uashauhsuahsuas, uau, uber, uberlandia, ucant, ucare, ucet, ucghhh, uchrissy, ucraine, ueah, uebel, ues, ufos, uggh, ughhghhh, ughhhh, ughhhhh,
ugle, uglier, uglies, uglky, uglyu, uheueheuhe, uhhm, uhhuh, uhmm, uhnn, uhoh, uhot, uhuh, ui, uin, uitzicht, uitzoeken, uje, ujhy, ujse, ujyrt,
ukdi, ulcer, ulgy, umay, umgestiegen, umim, umkc, umlaute, ummmh, ummmmmm, ummmnope, ummmyeah, ums, unabated, unadvertised,
unaltra, unattractive, unavailable, unbelievable, unbutton, unbuttoning, unc, uncaring, uncertain, uncles, uncomfy, uncontrolable, unconventional,
uncoopertive, uncultured, unda, undeer, undees, underagd, underbreath, undercover, underdone, underneith, underpaid, underpay, underrated,
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undersand, underscores, understa, understandeable, understandful, understands, underswtand, undertand, undertant, undertrazten, undress,
undrinked, unduly, uneasy, unemployed, unethical, unexceptional, unexperienced, unf, unfair, unfairly, unfinished, unfortuantely, unfortuitly,
unfortunatel, unfortunatelty, unfortunelz, unfortunetly, unfotunately, unfreeze, unfriedly, unfriendly, unfroze, unfunfunfunfunf, ungf,
ungghhghghghghhghghg, ungh, ungly, unhand, unholy, uniqe, univ, universities, universtiy, univesity, unkind, unknown, unles, unlike, unloved,
unorthodox, unos, unpolite, unprecedented, unreachable, unrealistic, unresponsive, uns, unsafe, unsanitary, unsatisfying, unsingle, unspeakable,
unspecific, unstable, unsupported, untied, untilli, unwanted, unwattery, unweighted, unwelcome, unwell, unwise, unwrapping, uoff, uomo, uor,
uou, uow, upcoming, update, upfront, upgraded, upgrading, upmyself, uppppp, upratat, uprigh, upsetting, upside, upstate, urantia, uranus,
urbanized, urchin, ureself, urgently, urinal, urk, urob, urry, uruguay, usage, usal, userfriendly, username, uses, ussr, ussually, ustomers, usualy,
uthink, utilize, utmost, utre, utter, uu, uuh, uuhhmm, uuhm, uum, uuu, uuuum, uuuuuuha, uw, uwhere, uwhile, uy, uyea, uyh, uyp, uyrtey, uyt,
uzdengsiu, uzsisakyti, uztex, vaaai, vacant, vacate, vace, vachier, vada, vado, vagatable, vaginer, vagrant, vais, vaistuku, vajay, valedictorian,
valnila, vam, vamanos, vamonos, vampires, vampy, vanden, vandoor, vanessa, vanishing, vanmiddag, vanno, vapdame, vargas, variations, varied,
vary, vas, vaza, vazio, vbee, vcera, vcs, vecer, veci, vedere, vedo, veeeeeeeeeeery, veeerry, veeery, vegable, vegans, vegatble, vegeble, vegelable,
vegematerians, vegemite, vegerville, vegesoup, vegeta, vegetabe, vegetal, vegetalble, vegetatablesoup, vegetbale, vegeteable, vegeterian, vegge,
veggi, vegiie, vegitarians, vegiterian, vegitible, vegs, vegstable, vegtabal, vegtebles, vegtetable, vegtible, vei, vein, vejetable, vejky, vel, velcome,
velma, veloce, velour, vending, venessa, venir, vent, venu, venus, verdammt, verdiend, verefy, vergtable, veritas, vermin, vermont, vermouth,
vero, verona, veronica, verpest, verre, verryyy, verschrikkelijk, versions, verspaten, versus, vertigo, vertipp, verw, verwijderen, verygood,
verymuch, veryp, veryprofesional, verything, vetarnarian, veu, veuillez, veyr, vi, vibrated, vicinity, vicious, victims, victory, vidanya, viel,
vielleicht, viene, viens, vies, vietnam, vietnamese, vijfeurow, viks, vile, vill, villagers, villain, villepin, villipin, vimoooooo, vindaloo, vineyard,
vinho, vioce, violate, violating, violation, violations, violence, violently, violet, vira, viraid, vireja, virggginnnia, virgiia, virgina, virginity,
virgins, virgo, virgule, virgy, virtral, visi, vision, visite, visitors, vist, visted, vistiek, visual, visuit, visuvieos, vitals, vitamines, vitual, vitvori, viu,
vivian, vixen, vm, vn, vocalizing, vocational, voce, voda, vodca, vodkia, voglio, voices, voici, vois, vokale, vola, volcano, volcanoes, volcanos,
volere, voll, volleyball, volume, volunteer, vom, vomits, vonner, voo, voodoo, vooraf, vooral, voovoo, vorrei, vos, vote, voted, votka, vou,
vouais, vouvo, vouvoy, vouz, vpred, vraag, vrachtwagen, vragen, vraiment, vreau, vrouw, vsak, vuile, vuilnisbak, vulger, vulnaberable,
vulnerable, vuo, vuol, vut, vvery, vvveeery, vvvv, vyber, vye, vyfajcit, vymet, vyno, vypada, vypadam, vypln, vystridame, vyzeru, waaaaaaah,
waaaaaah, waaaaaahooooo, waaaaaatteeerrrrr, waaaah, waaaat, waaaattteeeeerrrrrrrr, waaaawww, waaaay, waaant, waah, waas, waater, waauw,
wablieft, wac, waccamaw, wacha, wacht, wachten, wack, wacko, wackos, wacky, wada, wadadadd, wadda, waddupo, wadyw, waer, wafer,
waffes, waft, wage, wager, waggle, wagon, wagtet, wahnting, wahter, wahtz, waiiitresss, waine, waintin, waistline, waistress, waita, waite,
waiterss, waitert, waitin, waitnes, waitr, waitreessssss, waitrel, waitrese, waitresse, waitressssss, waitriss, waitrness, waits, waity, waka,
wakakaka, wakakookoos, wakeboarding, waldo, walet, walkabouts, wallbanger, walled, waller, wallhax, wallop, wallpapers, wally, walnut, wals,
wanana, wanb, wanda, wandered, wangsturbates, wanmt, wannabe, wannae, wanrt, wanst, wantd, wantg, wantin, wantme, wantn, wantsh, wantt,
wantv, waqlcome, wardrobe, warer, wares, warlock, warner, warped, warrants, wars, wasa, wasabi, wasatch, wasdelivered, washin, washrooms,
wasitress, wasn, wasnrt, wasre, wass, wasser, watches, watchin, watchoutt, watchu, watedr, wateer, watering, waterll, watermelon, waterrr,
waterskiing, waterw, waterwater, watet, wathc, wati, watier, watiressing, watn, watning, watressing, watwater, waugh, wavelength, wawaw,
wawwww, wayer, waygu, wayne, wayyyyy, wazit, wazup, wazzzzzup, wbats, wbeer, wd, wdaw, wddadsadwasdw, wdddddd, wdf, wea, weakk,
weakling, weakly, weakness, weapon, weapons, wears, webdesigner, webkinz, webs, wecome, wedge, wedges, wedstrijdje, weeeeeeeee,
weeeeeeeeeeee, weeewee, weekday, weell, weely, weep, weewee, wegetable, wegresto, wegwilt, wehave, weher, weighing, weighs, weighted,
weiler, weill, weinerscnizel, weirdd, weirder, weirding, weirdly, weired, weit, weiter, weither, weitres, wekk, welc, welccome, welckome,
welcolm, welcombe, welcomed, welcomer, welcomne, welcomw, welcomye, welecome, welkcome, welkom, welkome, welle, welli, welligton,
wellington, wells, wellyour, welocome, welome, welsh, welsome, wendy, wene, wensen, wenu, weredid, werein, weren, werewolf, werk, werkt,
weso, westham, westminster, weted, wether, weva, wfillet, wftr, wghat, wgrilled, whaddup, whadya, whagt, whahah, whahahahaha, whait, whao,
whard, whare, whart, whassup, whast, whasup, whatcan, whatchu, whateever, whatev, whateva, whateverrr, whateves, whatis, whatkind,
whatplease, whatsoever, whatss, whatta, whattaya, whatts, whatttt, whatup, whatw, wheaties, wheats, wheel, whehe, wheiihhuu, whel, whell,
whello, whenevr, wheok, whereabouts, wheree, wherehey, wherere, wherethe, whereva, wherevers, wherez, whers, wherw, wheuai, whewwww,
wheyyyyyy, whez, whhats, whhere, whia, whic, whight, whihc, whims, whin, whinecard, whinte, whiot, whiskee, whiskees, whiskeyy, whisper,
whiteboy, whitehous, whitewater, whitie, whiz, whn, whoaaa, whoaaaaa, whoamg, whoe, whohoooooo, wholw, whom, whone, whoo, whoohoo,
whooohooo, whoooohoooo, whoooooooo, whooper, whoopin, whooping, whoopsie, whoopsies, whoosh, whop, whoppers, whops, whorecake,
whorehouse, whove, whrecd, whreever, whrit, whrre, whrrn, whte, whtie, whwite, whya, whyeah, whyll, whywhy, wiatress, wibne, wiccan, wice,
widdower, widely, widen, wideset, wiedersen, wieeeeee, wien, wiener, wierdest, wierdos, wiet, wiew, wifes, wifey, wiggle, wiggling, wihe,
wihte, wiiiii, wiiiiii, wiiiiiiii, wiiiiiiiiiiieiieieieieiie, wiiiine, wiill, wijne, wijss, wilbots, wile, wilkommen, willafter, willen, willfollo, willies,
willkeep, willleave, willllll, willmove, willplease, wilting, wimminz, wimp, winbe, wince, winde, winding, windo, windowtable, winebeer,
wineglass, winehehehe, winem, winenow, winewould, winey, winke, winkel, winking, winnebago, winnecone, winner, winnie, winnings, wins,
wiper, wird, wiser, wishing, wiski, witchcraft, withdraw, wither, withh, withlocally, withmarinara, withme, withou, withour, witht, withthat,
withu, witj, witnessed, witress, witty, wity, wityig, wiull, wiz, wizards, wjhere, wjite, wk, wkd, wl, wlcom, wld, wlecome, wll, wlw, wna, wnana,
wnjoy, wnted, woh, wohaaa, wohne, woiuld, wojoo, wokaj, wokay, wokring, woldnt, wolverhamton, wolverhapton, womb, womon, wondeful,
wong, wooden, woodridge, woofer, woogy, wooho, woohooooooo, woood, wooooooo, woooooooooahhhhhh, wooooooow, woooot, woooozy,
wooops, woop, wooper, woould, woozie, woozily, woozy, wopuld, wor, worcheshire, wording, worgink, workd, workk, workmanship, worktop,
worldfamous, worm, worn, worng, worrisome, worship, worshipped, worss, worte, worthwhile, wotcha, woter, wotr, wotu, wotumake, woudlnt,
woulddo, woulde, wouldlike, wouldt, wouldu, wouled, woulf, woulld, wouold, wowould, woww, woyld, wqanted, wqater, wqith, wqould, wqqq,
wraaaargh, wrap, wrapping, wrath, wred, wrench, wrestled, wrestling, wrinkled, wrinkly, writer, wrok, wron, wrond, wrongly, wrongthing,
wroong, wrry, wrs, wrting, wsad, wsee, wsoup, wsswjsut, wtare, wtc, wtdlol, wtffffff, wtffffffff, wtffffffffffff, wtfhax, wtfomgbbqhungry,
wthank, wtih, wtv, wudnt, wuise, wuit, wuite, wunderful, wurde, wurst, wuts, wuu, wuv, wve, wvegetable, wwahahhahahhahhahahhahahhahah,
wwas, wwatter, wwelcome, wwwhy, wwwpardon, wwwthanks, wwww, wwwwaawdasdsa, wwwwww, wxcept, wz, xan, xant, xau, xc, xces,
xchoice, xcomment, xcould, xczv, xiao, xie, xmm, xome, xomg, xoxoxox, xxxoxox, xxxxxxxxxxxxx, yae, yahear, yahoooooooo, yaknow, yale,
yam, yammm, yan, yang, yankee, yankees, yao, yaq, yarbles, yarr, yarrr, yarrrr, yarrrrrr, yaty, yaya, yayayay, yayayayayay, yayyyy, ybe, ybeer,
ycake, ycan, ycool, ycrydiang, ydamn, yeaaa, yeaaaa, yeaaaay, yeaarghh, yeaer, yeahdont, yeahhhhh, yeahlolz, yearold, yearssince, yech,
yeeaaaayyyy, yeeah, yeeeah, yeeeeaaaaahhhhhh, yeeeeaaaahh, yeeeeeees, yeeeeees, yeeees, yeeeh, yeees, yeeess, yeehaw, yeep, yees,
yegeshmesh, yehh, yehtr, yellin, yello, yellowtail, yells, yeowch, yepper, yeppers, yerba, yerr, yerrr, yers, yesa, yeserr, yeshh, yeshhh, yesiiree,
yesits, yesj, yessm, yesssss, yessum, yesterdad, yesy, yesyyesyes, yetr, yew, yews, yez, yfine, yfirs, ygiqefy, ygood, ygot, yhaha, yhahaha, yhe,
yhere, yhour, yin, ying, yipee, yippy, yits, yjanx, ykno, ylet, ymmmm, ymuch, yoe, yof, yoface, yoga, yoh, yok, yoor, yoou, yop, yorkies, yorself,
yorur, yoshi, yot, yotch, youant, youanything, youbitch, youdont, youfor, youfoudnmeeeee, youhave, youhou, youir, youive, youj, youknowwhat,
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youl, youlater, youlll, youmiss, youmuch, youmust, youn, younglady, youor, youqueer, yourbill, yourdinner, yourelf, youreready, yourflowers,
yourjob, yourl, yourll, yourmeal, yourphone, yourr, yourselves, yourslef, yourslf, yourtself, youslf, yousoon, yousth, yout, youtr, youtrying,
youur, youuuu, youuve, youv, youyes, youz, yow, yoyo, yoyooyoyoyooyoyoyooy, yoyoyo, yoyoyoyo, yozgyfpiejhak, yrah, yre, yrself, yse, ysee,
yses, ysounds, yt, ytdh, yte, ythanks, ythanx, ythe, ything, ythose, ytou, ytr, ytre, ytyou, yuengling, yugfdrytrgurh, yugopslavia, yull, yuma,
yumaro, yumi, yumme, yummers, yummmmmmm, yummu, yummyh, yummyz, yumo, yumy, yung, yuns, yuopu, yuore, yuour, yups, yurself,
yvery, ywater, ywould, yyeaaargghhh, yyoull, yyour, yyum, yyuup, yyy, yyyeesss, yzes, zaak, zabiram, zac, zach, zadne, zadok, zahraju,
zajebem, zakazni, zakaznika, zalm, zambia, zant, zaplaceny, zaratal, zat, zdrham, zdrhnul, zdrhol, zdrowie, zead, zee, zeeland, zeggen, zeke,
zeker, zelda, zenska, zer, zerg, zergling, zest, zesty, zicht, zie, zien, zij, zillionth, zimbaweian, zin, zinc, zing, zinking, zip, zippers, zippy, zips,
zis, zjem, znamena, znas, zober, zoek, zoltan, zomblebees, zon, zonder, zoo, zooom, zoooom, zorarastrian, zould, zours, zourself, zouuu, zowat,
zozerem, zqit, zqter, zs, zu, zucchini, zullen, zune, zuppa, zuppes, zuvaj, zvietnam, zwaar, zweed, zxxoooooooooooooo, zzz, zzzz, zzzzz, zzzzzz,
zzzzzzzz, zzzzzzzzzz, zzzzzzzzzzzzzzzzzzzz
120
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Learning Plan Networks in Conversational Video

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