humor-computers.ppt 928KB Jun 23 2011 12:06:58 PM

Computers and Humor

by Don L. F. Nilsen

and Alleen Pace Nilsen

KURT VONNEGUT ON THE INTERNET

In August of 1997 a piece appeared on the

Internet by Kurt Vonnegut.

When Vonnegut’s wife was given a copy of the

article she was so pleased with her clever

husband that she forwarded a copy to their

children.

Vonnegut said that it was “funny and wise and

charming,” but he never wrote it.

The article had actually been published by Mary Schmich in the Chicago Tribune and then picked up and redistributed by a computer hacker.

Ian Fisher of The New York Times said that as long as readers

thought the piece was Vonnegut’s, they viewed the Internet as a wonderful tool that could keep people in touch with each other. But when they learned it was a hoax, their perception of the

internet changed. The internet was now an unreliable hotbed of hoaxes and wild-eyed conspiracies.

Probably both opinions are true.

TWENTE, NETHERLANDS

–

_{Every year there is an annual workshop on}

Language Technology at the University of

Twente.

–

_{In 1996 this workshop was devoted to}

“Automatic Interpretation and Generation

of Verbal Humor.”

–

The papers at this conference had such

titles as:

• “Why do People Use Irony?”

• “Password Swordfish: Verbal Humour in the Interface.”

• “Computer Implementation of the General Theory of Verbal Humor.”

• “Humor Theory beyond Jokes.” • “Speculations on Story Puns.”

• “Relevance Theory and Humorous Interpretations.” • “What Sort of a Speech Act is the Joke?”

• “A Neural Resolution of the Incongruity-Resolution Theory of Humor”

• “Humorous Analogy: Modeling the Devil’s Dictionary.” • “Why Is a Riddle Not Like a Metaphor?” and

• “An Attempt at Natural Humor from a Natural Language Robot.” • (Nilsen and Nilsen 98)

Computer Generated Humor:

Apple’s Joke Teller

• Given the command, “Computer, tell me a joke,” this is one response:

• COMPUTER: Knock, knock. • YOU: Who’s there.

• COMPUTER: Thistle. • YOU: Thistle who?

• COMPUTER: “Thistle be my last knock knock joke. (Hemplemann, 333)

Natural Language Processing:

Suspension of Disbelief

•

General Principle: “If your system can’t

do natural language, force the user to

use your version of an artificial

language and make it feel like natural

language as much as necessary”

Computers with a Sense of Humor

•

_{Kim Binstead says that humor can help}

“make clarification queries less repetitive,

statements of ignorance more acceptable,

and error messages less patronizing.”

(Hempelmann 336)

•

_{John Morkes et. al. demonstrate that}

computer systems that employ humor are

viewed as “more likable and competent”

(Morkes 215).

FACS: Facial Action Coding System

• “Based on an anatomical analysis of facial action, FACS

describes facial expressions and movements and in a second step relates them to emotions.”

• FACS distinguishes between different types of smiles and laughs by using such parameters as frequency, intensity, duration, and symmetry.

• Paul Ekman and Wallace Friesen are using the FACS to build gestural facial and bodily expressions into computer programs. • FACS has also been used by the movie industry in such films

as Shrek and Toy Story.

JAPE: Joke Analysis and Production Engine

•

_{Kim Binstead and Graeme Ritchie are using}

the JAPE system to generate humor.

•

However, “JAPE’s joke analysis and

production engine is merely a punning riddle

generator. It is not “generative” in Noam

Chomsky’s sense of the word.

A JAPE Joke

•

JAPE would use information like the following to

produce this joke:

•

(i) “cereal” IS-A “breakfast food”

•

(ii) “murderer” IS-A “killer”

•

(iii) “cereal” SOUNDS-LIKE “serial”

•

(iv) “serial klller” is a meaningful phrase

•

Q: What do you get when you cross a breakfast food

with a murderer?

STANDUP: Interactive Riddle Builder

•

STANDUP has a larger resource size than JAPE.

•

STANDUP is designed to help children with language

problems stay on task.

•

Children use the STANDUP program to produce

riddles, and the humor in the program keeps the

children interested and active.

•

But STANDUP has basically the same level of

computer sophistication as does JAPE.

How to Make a Computer Laugh:

Computer Recognition of One-Liners

•

Rada Mihalcea, Stephen Pulman and

Carlo Strapparava are looking for

correspondences between the surface

structure and the text meanings to see

which ones correlate with humorous

and non-humorous texts.

Humorous Signals

Human Centeredness & Polarity Orientation

• The expressions that correlate with humor can be categorized as:

• Human-Centric Vocabulary (pronouns…) • Negative Evaluations (“wrong,” “error”…)

• Professional Communities (“lawyers,” “programmers”…) • Negative Traits (“ignorance,” “lying”…)

Fuzzy Logic

•

_{Hans Wim Tinholt and Anton Nijholt are}

working with “fuzzy logic” and “anaphoric

ambiguity” to investigate sentences like,

“The cops arrested the demonstrators

because they were violent.”

•

_{Identifying the ambiguity is relatively easy,}

but deciding which ambiguity is humorous is

much more difficult.

EIGENTASTE



JESTER

•

_{Eigentaste is a “constant time collaborative}

filtering algorithm.”

•

Dhruv Gupta, Mark Digiovanni, Hiro Narita,

and Ken Goldberg are adapting Eigentaste

into JESTER, which is a system that can

actually evaluate the jokes in a large

database.

GTVH: General Theory of Verbal Humor

LIBJOG: Lightbulb-Joke Generator

•

Victor Raskin and Salvatore Attardo are using a

modification of GTVH called LIBJOG to produce

light-bulb jokes. The authors are aware that their

humor generator has “zero intelligence.”

•

_{“In fact, the main thrust of LIBJOG was to expose}

the inadequacy of such systems (as JAPE) and to

emphasize the need to integrate fully formalized

large-scale knowledge resources in a scalable model

of computational humor.”

SSTH: Semantic Script Theory of Humor

and the HAHAcronym Generator

•

The HAHAcronym Generator is loosely based on

Raskin and Attardo’s SSTH.

•

“Using WordNet Domains, like Medicine or

Linguistics, antonymy relations between the

domains, like Religion vs. Technology, as well as

several other supporting resources, they create

funny interpretations for acronyms.”

•

_{“MIT becomes “Mythical Institute of Theology.”}

SSTH: Semantic Script Theory of Humor:

•

SSTH shows script overlap and script

oppositeness.

•

_{“But when the theory is quoted,}

exclusive attention is usually paid to

script opposition, while overlap is, at

the most, quietly understood to be

involved.”

SSTH and Ontological Semantics

•

_{For the Semantic Script Theory of Humor to}

be really effective, it must include ontological

semantics.

•

_{But ontological semantics needs to}

systematically deal with the information

found in dictionaries, encyclopedias,

thesauruses, and many other types of

reference books.

Using Ontological Semantics to Generate a Joke

•

In his “Computational Humor: Beyond

the Pun?” Christian Hempelmann gives

seven pages of rigorous and

systematic details to generate the

following joke:

•

_{Q: What did the egg say in the}

monastery?

Joke vs. Wordplay

•

For people who fail to see the overlap in a

joke, it isn’t a joke at all. It is merely word

play.

•

“Given that humans are desperately good

disambiguators with vast semantic networks

available to them, as well as excellent

pragmatic interpreters, we seek any kind of

semantic overlap to be able to handle the

phonological (quasi-)ambiguity as humor,

even if mere wordplay was intended.”

Klangspiel: Play with Sounds, vs.

Sinnspiel: Play with Meanings

•

_{“What adds to the confusion is that}

non-humorous wordplay, like rhyming, can be

enjoyed aesthetically, and this enjoyment can

be confused with the enjoyment derived from

humor.”

•

_{“The belief on the part of a joker that he or}

she can get away with pure ‘Klangspiel’ is

what earns bad puns (i.e. groaners) a pariah

status in the family of jokes.”

Ynperfect Pun Selector

•

In an article entitled, “Ynperfect Pun Selector for

Computational Humor,” Christian Hempelmann gives

the following joke:

•

A. Knock knock.B. Who’s there?

•

A. Cantaloupe. B. Cantaloupe who?

•

A. Can’t elope tonight—Dad’s got the car.

•

Hempelmann also considered bilingual punning, as

in, “Those who jump off a Paris bridge are in Seine”

(Hempelmann 342-343).

Willing Suspension of Disbelief in A Joke

• Samuel Coleridge said that the two key elements of poetry are “a human interest and a semblance of truth sufficient to procure for these shadows of imagination that willing suspension of

disbelief for the moment, which constitutes poetic faith.”

• Hempelmann considers a joke, as an aesthetic text, to be a specific type of poetry. But the joke also requires opposition and incongruity.

• Willing suspension of disbelief is required “to reconcile this incongruity and at least playfully, make it spuriously

appropriate.”

Verbal Literacy

BOTTOM-UP AND TOP-DOWN PROCESSING

•

_{Bottom-up processing relates to decoding.}

You start with the actual sounds, letters,

morphemes, etc. and figure out the words,

phrases, clauses, sentences, paragraphs,

etc.

•

_{Top-down processing is based on reasoning.}

You make a generalization and see how well

the sounds, letters, morphemes, etc. support

your generalization.

•

Top-down reasoning is powerful, but it

can be dangerous if it is not

accompanied by bottom-up reasoning.

•

For example, Otto Jesperson assumed

that men were better thinkers than

women.

•

He conducted an experiment in which

men and women read a story and were

given a quiz.

•

_{The women responded more quickly and more}

accurately than the men, which was not what

Jacobson had expected.

•

_{So he concluded that women’s minds have}

“vacant chambers” that men’s minds don’t

have.

•

This allowed Jacobson to account for his

evidence while at the same time not

disproving his original hypothesis that men

were better thinkers than women.

Boolean Algebra

•

_{Christie Davies says Boolean algebra}

“enables users to hide problems and

assumptions behind algebraic symbols.

•

_{You can not easily turn words into numbers.}

•

_{Those who try to do so usually do not}

understand either.”

COMPUTER WORDS AND METAPHORS

•

COMPUTER WORDS: bits, bytes, code police,

cyberspace, future shock, hackers, hard copy,

menu, third wave, user-friendly

•

COMPUTER METAPHORS: 42, bug, cookies, GIGO,

great runes, heavy wizardry, Lotus Software, Melvyl

(California Library System), a sagan, snail mail,

Sorcerer’s Apprentice mode, Trojan Horse, USLT,

vulcan nerve pinch, web, YABA compatible

CONCORDANCE

•

A Concordance checks for word

frequency and word associations.

•

Word associations are determined by

providing a window of three words

(more or less) on each side of the

targeted word.

ELIZA

•

_{ELIZA is a program designed to pass the}

turing test. If a person communicating with

ELIZA cannot tell whether or not ELIZA is a

human, then ELIZA passes the turing test.

•

ELIZA claims to be a psychiatrist talking to a

patient. ELIZA, like a psychiatrist, is

attempting to get rather than give

information:

•

_{PATIENT: Men are all alike.}

•

_{ELIZA: In what way?}

•

PATIENT: They’re always bugging me about

something or other.

•

_{ELIZA: Can you think of a specific example?}

•

_{PATIENT: Well, my boyfriend made me come}

here.

•

ELIZA: Your boyfriend made you come here?

•

PATIENT: He says I’m depressed much of the

time.

•

ELIZA: I am sorry to hear that you are

depressed.

•

Victor Raskin fooled ELIZA by saying,

“I don’t like my mother and I don’t want

to talk about her.”

•

_{ELIZA responded, “Tell me more about}

your mother.

CIRCUIT FIX-IT SHOP

• Twenty-five years after ELIZA, a program named “Circuit Fix-It Shop” was developed:

• COMPUTER: This is the circuit fix-it shop. How may I help you?

• TECHNICIAN: I need to fix the RS111 circuit.

• COMPUTER: I am familiar with that circuit. The LED is supposed to be displaying alternatively blinking one and seven.

•

_{COMPUTER: In the middle right of the blue}

region is the LED.

•

TECHNICIAN:

No light.

•

_{COMPUTER: What is the switch at when the}

LED is off?

•

TECHNICIAN: The switch is down.

MORE SOPHISTICATED PROGRAMS

•

_{Today more sophisticated programs are}

needed. One such program is the little

Paperclip guy that answers questions in

Microsoft Word.

•

Another sophisticated program is “Script

Model Grammar” designed by Roger Schank

and Robert Abelson and modified by linguist

Victor Raskin and others at Purdue

SAM: SCRIPT APPLIER MECHANISM

•

_{Of course sentences need to be parsed in}

Artificial Intelligence. But constituents larger

than a sentence must be parsed as well.

•

_{One of the devices for doing this discourse}

•

Note that a play or a movie has a script

for the actors to follow.

•

The script in Artificial Intelligence is the

same, but it is much simpler. It is a

“mundane script.”

•

The “Restaurant Script,” for example

involves a customer, a server, a

cashier, etc.

Props in the “Restaurant Script” include the restaurant, the table, the menu, the food, the check, the payment, the tip, etc.

The sequence of actions is as follows: 1. Customer goes to restaurant.

2. Customer goes to table. 3. Server brings menu.

4. Customer orders food. 5. Server brings food. 6. Customer eats food. 7. Server brings check.

8. Customer leaves tip for server.

9. Customer gives payment to cashier. 10. Customer leaves restaurant.

(Hendrix and Sacerdote 654) (Nilsen Nilsen & Combs 199)

•

_{There are two exciting things about the Script}

Applier Mechanism. First, it is able to spot

anything that is missing, added, or out of

place in the sequence of events and ask,

“What’s up.”

•

_{Second, it is able to handle two scripts at the}

same time, so that it is capable of dealing

with jokes, language play, satire, irony,

sarcasm, parody, paradox and double

entendre in general.

PARSING PROBLEMS

• GARDEN PATH:

• The horse raced past the barn fell.

• After the child visited the doctor prescribed a course of injections.

• The doctor said the patient will die yesterday.

• EMBEDDING: “Never imagine yourself not to be otherwise than what it might appear to others…to be otherwise.”

• (Lewis Carroll’s Alice’s Adventures in Wonderland) (Fromkin Rodman Hyams 365, 373)

RIGHT-BRANCHING VS. EMBEDDING

•

RIGHT BRANCHING: This is the dog that worried the

cat that killed the rat that ate the malt that lay in the

house that Jack built.

•

EMBEDDING: Jack built the house that the malt that

the rat that the cat that the dog worried killed ate lay

in.

•

_{NOTE Multiple embedding is OK for a computer, but}

not OK for the human brain.

•

_{ANOMALOUS WORDS: A sniggle blick is}

procking a slar.

•

METANALYSIS (incorrect phrase breaking):

•

grade A vs. grey day

•

night rate vs. nitrate

(Fromkin Rodman Hyams 368, 370)

•

NOTE: English “adder” and “apron” were

borrowed incorrectly from the French

expressions “un nadder” and “un naperon”

respectively

•

_{AMBIGUOUS SYNTAX IN NEWSPAPER}

HEADLINES:

•

Teacher Strikes Idle Kids

•

_{Enraged Cow Injures Farmer with Ax}

•

_{Killer Sentenced to Die for Second Time in}

10 Years

•

_{Stolen Painting Found by Tree}

REAL-WORLD KNOWLEDGE

•

Explain why the following sentences are ambiguous to

a computer but not to a human:

•

A cheesecake was on the table. It was delicious and

was soon eaten.

•

SIGN IN A CHURCH: For those of you who have

children and don’t know it, we have a nursery

downstairs.

•

NEWSPAPER AD: Our bikinis are exciting; they are

simply the tops.

• ANTISMOKING CAMPAIGN SLOGAN: It’s time we make smoking history.

• Do you know the time?

• Concerned with spreading violence, the president called a press conference.

• The ladies of the church have cast off clothing of every kind and they may be seen in the church basement Friday.

AMBIGUOUS NEWSPAPER HEADLINES

•

Red Tape Holds Up New Bridge

•

Kids Make Nutritious Snacks

•

_{Sex Education Delayed, Teachers}

Request Training

SEMANTIC PRIMING

•

In the human brain, the word “doctor” is more easily

and more completely processed if it is preceded by

“nurse” than if it is preceded by “flower.”

•

This is because “doctor” and “nurse” “are located in

the same part of the mental lexicon.”

(Fromkin Rodman Hyams 371)

•

_{This same feature could easily be built into Artificial}

SPEECH RECOGNITION

& SPEECH SYNTHESIS

• “Computational phonetics and phonology has two concerns. The first is with programming computers to analyze the speech signal into its component phones and phonemes.

• The second is to send the proper signals to an electronic

speaker so that it enunciates the phones of the language and combines them into morphemes and words.

• The first of these is speech recognition; the second is speech synthesis.”

•

“Machines which…imitate human

speech, are the most difficult to

construct, so many are the agencies

engaged in uttering even a single word

—so many are the inflections and

variations of tone and articulation, that

the mechanician finds his ingenuity

taxed to the utmost to imitate them.”

• TO SYNTHESIZE SPEECH:

• 1. Start with a tone at the same frequency as vibrating vocal cords (higher if a woman’s or child’s voice is being synthesized, lower for a man’s)

• 2. Emphasize the harmonics corresponding to the formants required for a particular vowel, liquid, or nasal quality.

• 3. Add hissing or buzzing for fricatives.

• 4. Add nasal resonances for nasal sounds.

• 5. Temporarily cut off sound to produce stops and affricates….

(Fromkin Rodman Hyams 386)

• A Sound Spectrogram will give an indication of some of the variables of analyzing or synthesizing speech:

SOUND SPECTROGRAM

SPELL CHECKER

I have a spelling checker.

It came with my PC.

It plane lee marks four my revue

Miss steaks aye can knot sea.

(Fromkin Rodman Hyams 381)

Explain why the spell checker is not

THEORIES AND MODELS

•

In The Physicist’s Conception of Nature, Manfred

Eigen said, “A theory has only the alternatives of

being right or wrong. A model has a third

possibility: it may be right, but irrelevant.”

(Fromkin Rodman Hyams 397)

•

Explain why a theory for Artificial Intelligence must

be rigorous and at the same time allow for language

play. In AI, are rigor and language play compatible

concepts or not?

TRANSLATION

•

_{“Translation is more than word-for-word}

replacement. Often there is no equivalent

word in the target language, and the order of

words may differ, as in translating from an

SVO language like English to an SOV

language like Japanese. There is also

difficulty in translating idioms, metaphors,

jargon, and so on.”

•

_{“Machine translation is often impeded by}

lexical and syntactic ambiguities, structural

disparities between the two languages,

morphological complexities, and other

cross-linguistic differences.”

(Fromkin Rodman Hyams 382)

•

_{In the following examples consider what}

information must be taken into consideration

for better machine translation:

• BUCHAREST HOTEL: The lift is being fixed for the next day. During that time we regret that you will be unbearable.

• SWISS NUNNERY HOSPITAL: The nuns harbor all diseases and have no respect for religion.

• GERMAN HOTEL: All the water has been passed by the manager. • ZURICH HOTEL: Because of the impropriety of entertaining

guests of the opposite sex in the bedroom, it is suggested that the lobby be used for this purpose.

• TURKEY: The government bans the smoking of children.

Having Fun with

Computer

1024

When Alan Schoenfeld of the University of

California at Berkeley attended a conference

on Artificial Intelligence, he was given Hotel

Room Number 1024.

Wow! he said.

1024 is 2 to the tenth power. It is a kilobyte.

ACRONYMS

•

Acronyms are so common in computer

terminology that programmers make fun of them.

•

“TLA” stands for “Three Letter Acronym.”

•

_{“YABA” stands for “Yet Another Bloody Acronym.”}

•

“YABA Compatible” means that the initials can be

pronounced easily, and are not ambiguous or

offensive.

CHAT GROUPS

• Linguist Susan Herring at the University of Texas, Arlington studied the humor in chat groups. Her results were as follows: • imaginary situations: 20 percent

• a mock persona: 14 percent • teasing: 13 percent

• irony: 6 percent

• name play: 5 percent • silliness: 4 percent

• real situations: 3 percent • riddles: 2 percent

• pretended misunderstandings: 2 percent • puns: 1 percent

EMOTICONS

• In conversation we can show our emotions, but on the internet this is difficult, so we use emoticons:

• :-) Smiling

• :-)))))))))) Really Smiling • ;-) Winking

• :-* Kissing • I-0 Yawning

• :-& Tongue-Tied • :’-{ Crying

• :-/ Undecided • :-II Angry

SCIENCE FICTION AND FANTASY

• Many computer terms come from Science Fiction and Fantasy:

• A huge network packet is a “Godzillagram” from Godzilla

• Teenage hackers are “Munchkins” from The Wizard of Oz

• A mischievious program is called a “wabbit” from Elmer Fudd’s “You wascawwy wabbit.”

• A program that repeats itself indefinitely is said to be in “Sorcerer’s Apprentice Mode” from Fantasia

• The meaning of life, truth, and everything is “42” from a computer in Douglas Adams’ Hitchhiker’s Guide to the Galaxy.

•

_{When someone asks for information that they}

can easily find themselves, the Cyber Police

might say, “UTSL.” This means “Use the

Source, Luke!” from Starwars.

•

Another word from Starwars is an “Obi-Wan

Error.” This comes from the name “Obi-Wan

Kenobi” and refers to an “off-by-one code,” as

in 2001: A Space Odyssey where the computer

is named “HAL.” This comes from “IBM” but

is the three letters before I, B, and M.

•

In computer terminology a soft boot refers to the

hitting of “Control,” “Alternate” and “Delete” at the

same time.

•

This is refered to as the “Vulcan Nerve Pinch” from

Star Trek.

•

“Droid” from “Android” has become a suffix in such

words as “trendroids,” who follow trends, and “sales

droids” who promise customers things that can not

be delivered or are useless.

•

The “code police” and “net police” are named after

the “thought police” in George Orwell’s 1984.

SIGNATURES

•

_{People like to create enigmatic and puzzling}

signatures. One user named Eddie follows

his signature with “Ceci n’est pas une

signature.”

•

This is an allusion to a painting of a pipe by

René Magritte with the disclaimer, “Ceci n’est

pas une pipe.”

!TEXT MESSAGING

Since numbers and letters require more than a single stroke on cell phones, acronyms are often used:

1337 (leet [elete])

AFAIK: As far as I know BFF: Best Friends Forever BTW: By the way

CUL or CUL8R: See you later FTW: For The Win

FYI: For Your Information

GIGO: Garbage In Garbage Out GFR: Grim File Reaper

I <3 you (I less than three you) IMHO (In My Humble Opinion) L8tr (later)

LOL: Lots of Laughs (Lolocaust, lolling and seriousing)

!!

POS: Parent Over Shoulder ROTF: Rolling on the Floor

ROTFLMAO: Rolling on the Floor Laughing My Ass Off RUOK: Are you OK?

STFU: (obscene)

TIA: Thanks in Advance TMI: Too Much Information woot

WTF: (obscene)

WYSIWYG: What you See Is What You Get and

BCNU: Be Seein’ you

!!!VIRUS JOKES

•

AT&T Virus: Every three minutes it

tells you what great service you are

getting.

•

_{MCI Virus: Every three minutes it}

reminds you that you’re paying too

much for the AT&T virus.

•

!!!Paul Revere Virus: This revolutionary

virus does not horse around. It warns

you of impending hard disk attack—

once if by LAN, twice if by C:>.

•

_{New World Order Virus: Probably}

harmless, but it makes a lot of people

really mad just thinking about it.

!!!Computer Humor Website

ANIMATOR VS. ANIMATION II:

http://www.metacafe.com/watch/689540/animator_vs_animation_2/

DAMN YOU AUTOCORRECT (JAY LENO SHOW):

http://damnyouautocorrect.com/7264/video-damn-you-autocorrect-featured-on-the-tonight-show-with-jay-leno/

THE THE IMPOTENCE OF PROOFREADING (TAYLOR MALI):

THE JOKING COMPUTER (UNIVERSITY OF ABERDEEN):

http://www.abdn.ac.uk/news/details-8719.php

LOLSPEAK:

http://www.speaklolspeak.com http://www.lolcatbible.com

MY BLACKBERRY’S NOT WORKING:

http://www.flixxy.com/my-blackberry-is-not-working.htm

TEXTING: Justin Long & Jimmy Kimmel:

http://www.youtube.com/watch?v=Afhk5VDCpb0&feature=fvwrel

TOP 50 POPULAR TEXT & CHAT ACRONYMS (NETLINGO):

Related PowerPoints

•

Movie Humor

•

Stand-Up Comedy

•

Television Humor

•

_{Urban Legends (in contrast to Tall Tales}

References:

Attardo, Salvatore. Humorous Texts: A Semantic and Pragmatic Analysis. New York, NY: Mouton de Gruyter, 2001.

Attardo, Salvatore. Linguistic Theory of Humor. New York, NY: Mouton de Gruyter, 1994.

Attardo, Salvatore. “The Semantic Foundations of Cognitive Theories of Humor.” HUMOR: International Journal of Humor Research 10.4

(1997): 395-420.

Attardo, Salvatore, Christian F. Hempelmann, and Sara Di Maio. “Script Oppositions and Logical Mechanisms: Modeling Incongruities and their Resolutions.” HUMOR: International Journal of Humor Research 15.1 (2002): 3-46.

Attardo, Salvatore, and Victor Raskin. “Script Theory Revis(it)ed: Joke Similarity and Joke Representation Model.” HUMOR: International Journal of Humor Research 4.3-4 (1991): 293-347.

Binstead, Kim. Using Humour to Make Natural Language Interfaces More Friendly. Unpublished MS Thesis, Edinburgh, Scotland: University of Edinburgh, 1995.

Binstead, Kim, Benjamin Bergen, Seana Coulson, Anton Nijholt, Oliviero Stock, Carlo Strapparava, Graeme Ritchie, Ruli Manurung, Helen Pain, Analu Waller, and Dave O’mara. “Computational Humor.” IEEE

Intelligent Systems 21.2 (2006): 59-69.

Binstead, Kim, and Graeme Ritchie. “An Implemented Model of Punning Riddles.” in Stock (2002): 633-638.

Binstead, Kim, and Graeme Ritchie. “Computational Rules for Generating Punning Riddles.” HUMOR: International Journal of Humor Research 10.1 (1997): 25-76.

Carrell, Amy. “Joke Competence and Humor Competence.” HUMOR: International Journal of Humor Research 10.2 (1997): 173-185. Chottiner, Sherman. Calculus Alive and Applied: For Business,

Clark, Michael. “Computers and the Humanist: The Myth and Reality.” The English Journal 76.4 (1987): 33-34.

Clark, Virginia, Paul Eschholz, and Alfred Rosa. Language: Readings in Language and Culture, 6th Edition. New York, NY: St. Martin’s Press, 1998.

Danet, B. Cyberpl@y: Communicating Online. Oxford, England, Berg, 2001.

Davies, Christie. “Understanding the Comparative Study of Humor” in Raskin (2008): 157-182.

Eschholz, Paul, Alfred Rosa, and Virginia Clark. Language Awareness: Readings for College Writers. New York, NY: Bedford/St. Martins, 2009.

Ekman, Paul, and Erika L. Rosenberg, eds. What the

Face Reveals: Basic and Applied Studies of

Spontaneous Expression Using the Facial Action

Coding System. Oxford, England: Oxford University

Press, 2005.

Ekman, Paul, and Wallace V. Friesen. The Facial Action

Coding System (FACS): A Technique for the

Measurement of Facial Action. Palo Alto, CA:

Consulting Psychologists Press, 1978.

English, Katharine, ed. Most Popular Web Sites: The

Best of the Net from A2Z. Indianapolis, IN: Lycos

Press, 1996.

Fromkin, Victoria, Robert Rodman, and Nina Hyams. “Language Processing: Humans and Computers.” An Introduction to

Language, 8th Edition. Boston, MA: Thomson Wadsworth, 2007, 363-408.

Goldberg, Ken, Theresa Roeder, Dhruv Gupta, and Chris Perkins. “Eigentaste: A Constant Time Collaborative Filtering Algorithm.” Information Retrieval 4.2 (2001): 133-151.

Gralla, Preston. How the Internet Works. Emoryville, CA: Ziff-Daivs Press, 1997.

Hempelmann, Christian F. “Computational Humor: Beyond the Pun?” in Raskin (2008) 333-360.

Hempelmann, Christian F. Paronomasic Puns: Target

Recoverability towards Automatic Generation. Unpublished PhD Dissertation, West Lafayette, IN: Purdue University, 2003.

Hempelmann, Christian F. “Script Opposition and Logical Mechanism in Punning.” HUMOR: International Journal of Humor Research 17.4 (2004): 381-392.

Hendrix, Gary G., and Earl D. Sacerdoti. “Natural-Languag Processing: The Field in Perspective.” in Language: Introductory Readings, 4th

edition. Eds. Virginia P. Clark, Paul A. Eslchholz and Alfred F. Rosa. New York, NY: St. Martin’s, 1985.

Holcomb, Christopher. “A Class of Clowns: Spontaneous Joking in Computer-Assisted Discussions. Computers and Composition 14.1 (1997): 3-18.

Hulstijn, Joris, and Anton Nijholt eds. Twente Workshop on Language Technology 12: Automatic Interpretation and Generation of Verbal Humor. Twente, Netherlands: Univ of Twente Dept of Computer Science, 1996.

Hurley, Kathleen Ann. “Humor and Technical Communication: The

Culture, The Texts, The Implications.” Unpublished Ph.D. Dissertation. Ames, IA: Iowa State University, 1996.

Kuipers, Giselinde. “‘Where was King Kong when we needed him?” Public Discourse, Digital Disaster Jokes, and the Functions of Laughter after 9/11.” The Journal of American Culture 28 (2005): 70-84.

Kuipers, Giselinde. “The Social Construction of Digital Danger: Debating, Defusing, and Inflating the Moral Dangers of Online Humor and

Pornography in the Netherlands and the United States.” New Media and Society 8.3 (2006): 379-400.

Ling, Rich, and Naomi S. Baron. “Text Messaging and IM: Linguistic Comparison of Americ an College Data.” Journal of Language and Social Psychology 26 (2007): 291-298.

Loehr, Dan. “An Attempt at Natural Humor from a Natural Language Robot.” in Hulsteijn and Nijholt (1996): 161-171.

Mihalcea, Rada, and Carlo Strappareva. “Computational Laughing:

Authomatic Recognition of Humorous One-Liners.” Proceedings of the XXVII Annual Meeting of the Cognitive Science Society, Stresa Italy, 2005: 1513-1518.

Mihalcea, Rada, and Stephen Pulman. “Characterizing Humour: An Exploration of Features in Humorous Texts.” Lecture Notes in Computer Science 4394 (2007): 337-347.

Moran, Joseph M., Gagan S. Wig, Reginald B. Adams, Jr., Petr Janata, and William M. Kelley. “Neural Correlates of Humor Detection and Appreciation.” Neuroimage 21.3 (2004): 1055-1060.

Morkes, John, Hadyn K. Kernal, and Clifford Nass. “Humor in Task-Oriented Computer Interactional and Computer-Mediated Communication.”

Human-Computer Interaction 14.4 (1999): 394-435.

Mulder, Matthijs P., and Anton Nijholt. Humour Research: State of the Art. Technical Report. Enschede, Netherlands: Universiteit Twente, 2002.

Nijholt, Anton. “Embodied Agents: A New Impetus to Humor Research.” in Stock (2002): 101-111.

Nilsen, Alleen Pace, and Don L. F. Nilsen. “Computer Humor,” and “Internet Influences.” Encyclopedia of 20th Century American Humor. Westport, CT: Greenwood, 2000, 97-100 and 165-168. Nilsen, Don L. F., Alleen Pace Nilsen, and Nathan H. Combs.

“Teaching a Computer to Speculate.” Computers and the Humanities. 22 (1988): 193-201.

Nilsen, Kelvin, and Alleen Pace Nilsen. “Literary Metaphors and Other Linguistic Innovations in Computer Language” (Clark, 166-176).

Nirenburg, Sergei, and Victor Raskin. Ontological Semantics. Cambridge, MA: MIT Press, 2004.

Oring, Elliott. Engaging Humor. Urbana, IL: University of Illinois Press, 2003.

Parkinson, Jane. “Business Humor: Boos and Bravos.”

Unpublished LIN 790 PowerPoint, Tempe, AZ: ASU, 2009. Paulos, John Allen. Mathematics and Humor. Chicago, IL:

University of Chicago Press, 1980.

Plester, B. A., and J. Sayers. “‘Taking the Piss’: Functions of Banter in the IT Industry.” HUMOR: International Journal of Humor Research 20.2 (2007): 157-187.

Popa, Diana, and Salvatore Attardo, eds. New Approaches to the Linguistics of Humor. Galati: Dunarea de Jos University Press, 2007.

Raskin, Victor. “Computer Implementation of the General Theory of Verbal Humor.” in Hulstijn (1996): 9-19.

Raskin, Victor. Primer of Humor Research. New York, NY: Mouton de Gruyter, 2008.

Raskin, Victor. “Quo Vadis Computational Humor? In Stock (2002): 31-46.

Raskin, Victor, ed. Semantic Mechanisms of Humor. Boston, MA: Reider/Kluwer, 1985.

Raskin, Victor, and Salvatore Attardo. “literalness and Non-bona-fide in Language: An Approach to Formal and

Computational Treatments of Humor.” Pragmatics and Cognition 2.1 (1994): 31-69.

Raymond, Eric S. The New Hacker’s Dictionary, 2nd Edition.

Cambridge, MA: MIT Press, 1993.

Ritchie, Graeme, Ruli Manurung, Helen Pain, Annalu Waller, and Dave O’Mara. “The STANDUP Interactive Riddle Builder.” IEEE Intelligent Systems 21.2 (2006): 67-69.

Roberts, Steven K. “Artificial Intelligence.” in Writing and Reading Across the Curriculum, 2nd Edition. Laurence Behrens and

Leonard J. Rosen. Boston, MA: Litle, Brown, 1985, 214-222. Rosch, Eleanor. “On the Internal Structure of Perceptual and

Semantic Categories.” in Cognitive Development and the

Acquisition of Language. Ed. T. Moore. New york, NY: Academic Press, 1973.

Rouzie, Albert. “Conversation and Carrying-on: Play, Conflict, and Serio-Ludic Discourse in Synchronous Computer

Conferencing.” College Composition and Communication 53.2 (2001): 251-299.

Ruch, Willibald, and Paul Ekman. “The Expressive Pattern of

Laughter.” in Emotions, Qualia, and Consciousness, ed. Alfred Kaszniak. Tokyo, Japan: Word Scientific Publisher, 2001, 426-443.

Schank, Roger C., and Robert Abelson. Scripts, Plans, Goals, and Understanding: An Inquiry Into Human Knowledge Structures. Hillsdale, NJ: Lawrence Erlbaum, 1977.

Shiffman, Limor. “Humor in the Age of Digital Reproduction: Continuity and Change in Internet-Based Comic Texts.” International Journal of Communication 1 (2007): 187-209. Shiffman, Limor, Stephen Coleman, and Stephen Ward. “Only

Joking? Online Humour in the 2005 UK General Election.”

Siegel, David. Creating Killer Web Sites. Indianapolis, IN: Hayden Books, 1996.

Stock, Oliviero, and Carlo Strapparava. “HAHAcronym: Humorous Agents for Humorous Acronyms.” in Stock (2002): 125-135.

Stock, Oliviero, Carlo Strapparava, and Anton Nijholt, eds. TWLT20: The April Fools’ Day Workshop on Computational Humor Enschede, Netherlands: Universiteit Twente, 2002. Tagliamonte, Sali A., and Derek Denis. “Linguistic Ruin? LOL!

Instant Messaging and Teen Language.” American Speech 83.1 (2001): 3-34.

Taylor, Julia M., and Lawrence J. Mazlack. “Multiple Component Computational Recognition of Children’s Jokes.” Proceedings of the International Conference on Systems, Man and

Cybernetics Proceedings (IEEE-SMC). Montreal, Canada, 2007, 1194-1199.

Taylor, Julia M., and Lawrence J. Mazlack. “Toward Computational Recognition of Humorous Intent.” Proceedings of the XXVII

Annual Meeting of the Cognitive Science Society. Stressa, Italy, 2005, 2166-2171.

Taylor, Julia M., Pavel Klinov, and Lawrence J. Mazlack. “A

Description Logic Based Approach to Computational Humor” in Popa and Attardo (2007): 167-179

Tinholt, Hans Wim, and Anton Nijholt. “Computational Humor:

Utilizing Cross-reference Ambiguity for Conversational Jokes.” in 7th International Workshop on Fuzzy Logic and Applications

(WILF 2007): Lecture Notes in Artificial Intelligence. Berlin, Germany: Springer Verlag, 2007, 477-483.

Weizenbaum, Joseph. “ELIZA: A Computer Program for the Study of Natural Language Communication between Man and

Oring, Elliott. Engaging Humor. Urbana, IL: University of Illinois Press, 2003.

Parkinson, Jane. “Business Humor: Boos and Bravos.”

Unpublished LIN 790 PowerPoint, Tempe, AZ: ASU, 2009. Paulos, John Allen. Mathematics and Humor. Chicago, IL:

University of Chicago Press, 1980.

Plester, B. A., and J. Sayers. “‘Taking the Piss’: Functions of Banter in the IT Industry.” HUMOR: International Journal of Humor Research 20.2 (2007): 157-187.

Popa, Diana, and Salvatore Attardo, eds. New Approaches to the Linguistics of Humor. Galati: Dunarea de Jos University Press, 2007.

Raskin, Victor. “Computer Implementation of the General Theory of Verbal Humor.” in Hulstijn (1996): 9-19.

Raskin, Victor. Primer of Humor Research. New York, NY: Mouton de Gruyter, 2008.

Raskin, Victor. “Quo Vadis Computational Humor? In Stock (2002): 31-46.

Raskin, Victor, ed. Semantic Mechanisms of Humor. Boston, MA: Reider/Kluwer, 1985.

Raskin, Victor, and Salvatore Attardo. “literalness and Non-bona-fide in Language: An Approach to Formal and

Computational Treatments of Humor.” Pragmatics and Cognition 2.1 (1994): 31-69.

Ritchie, Graeme, Ruli Manurung, Helen Pain, Annalu Waller, and Dave O’Mara. “The STANDUP Interactive Riddle Builder.” IEEE Intelligent Systems 21.2 (2006): 67-69.

Roberts, Steven K. “Artificial Intelligence.” in Writing and Reading Across the Curriculum, 2nd Edition. Laurence Behrens and

Leonard J. Rosen. Boston, MA: Litle, Brown, 1985, 214-222. Rosch, Eleanor. “On the Internal Structure of Perceptual and

Semantic Categories.” in Cognitive Development and the

Acquisition of Language. Ed. T. Moore. New york, NY: Academic Press, 1973.

Rouzie, Albert. “Conversation and Carrying-on: Play, Conflict, and Serio-Ludic Discourse in Synchronous Computer

Conferencing.” College Composition and Communication 53.2 (2001): 251-299.

Ruch, Willibald, and Paul Ekman. “The Expressive Pattern of

Laughter.” in Emotions, Qualia, and Consciousness, ed. Alfred Kaszniak. Tokyo, Japan: Word Scientific Publisher, 2001, 426-443.

Schank, Roger C., and Robert Abelson. Scripts, Plans, Goals, and Understanding: An Inquiry Into Human Knowledge Structures. Hillsdale, NJ: Lawrence Erlbaum, 1977.

Shiffman, Limor. “Humor in the Age of Digital Reproduction: Continuity and Change in Internet-Based Comic Texts.” International Journal of Communication 1 (2007): 187-209. Shiffman, Limor, Stephen Coleman, and Stephen Ward. “Only

Joking? Online Humour in the 2005 UK General Election.”

Siegel, David. Creating Killer Web Sites. Indianapolis, IN: Hayden Books, 1996.

Stock, Oliviero, and Carlo Strapparava. “HAHAcronym: Humorous Agents for Humorous Acronyms.” in Stock (2002): 125-135.

Stock, Oliviero, Carlo Strapparava, and Anton Nijholt, eds. TWLT20: The April Fools’ Day Workshop on Computational Humor Enschede, Netherlands: Universiteit Twente, 2002. Tagliamonte, Sali A., and Derek Denis. “Linguistic Ruin? LOL!

Instant Messaging and Teen Language.” American Speech 83.1 (2001): 3-34.

Taylor, Julia M., and Lawrence J. Mazlack. “Multiple Component Computational Recognition of Children’s Jokes.” Proceedings of the International Conference on Systems, Man and

Cybernetics Proceedings (IEEE-SMC). Montreal, Canada, 2007, 1194-1199.

Taylor, Julia M., and Lawrence J. Mazlack. “Toward Computational Recognition of Humorous Intent.” Proceedings of the XXVII

Annual Meeting of the Cognitive Science Society. Stressa, Italy, 2005, 2166-2171.

Taylor, Julia M., Pavel Klinov, and Lawrence J. Mazlack. “A

Description Logic Based Approach to Computational Humor” in Popa and Attardo (2007): 167-179

Tinholt, Hans Wim, and Anton Nijholt. “Computational Humor:

Utilizing Cross-reference Ambiguity for Conversational Jokes.” in 7th International Workshop on Fuzzy Logic and Applications

(WILF 2007): Lecture Notes in Artificial Intelligence. Berlin, Germany: Springer Verlag, 2007, 477-483.

Weizenbaum, Joseph. “ELIZA: A Computer Program for the Study of Natural Language Communication between Man and