Fromkin Rodman Hyams 403

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Transcript Fromkin Rodman Hyams 403

Computers and Humor
by Don L. F. Nilsen
and Alleen Pace Nilsen
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High Learning Curve
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IT Support
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How Computers Have Affected the World
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Bill Gates
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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.
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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.
(Nilsen & Nilsen 168)
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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:
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• “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)
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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)
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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”
(Hempelmann 335).
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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).
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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.
(Hempelmann 337)
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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.
(Hempelmann 337)
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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?
• A: A cereal killer.
(Hempelmann 338)
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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.
(Hempelmann 340)
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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.
(Hempelmann 340)
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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”…)
(Hempelmann 340)
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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.
(Hempelmann 341)
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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.
(Hempelmann 341).
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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.”
(Hempelmann 338)
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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.”
(Hempelmann 339)
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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.”
(Hempelmann 342)
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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.
(Hempelmann 347)
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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?
• A: Out of the frying pan, into the friar.
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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.”
(Hempelmann 346)
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Klangspiel: Play with Sounds, vs.
Sinnspiel: Play with Meanings
• “What adds to the confusion is that nonhumorous 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.”
(Hempelmann 346).
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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).
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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.”
• Note that this same willing suspension of disbelief is required
in religion and in magic (Hempelmann 344-345).
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Verbal Literacy
vs. Number Literacy
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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.
(Fromkin Rodman Hyams 369)
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• 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.
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• 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.
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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.”
(Davies [2008]: 178)
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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
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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.
(Fromkin Rodman Hyams 379)
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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:
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• 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.
(Fromkin Rodman Hyams 389)
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• 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.
(Nilsen Nilsen & Combs 199)
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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.
• TECHNICIAN: Where is the LED?
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• 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.
• COMPUTER: You put up the switch.
(Fromkin Rodman Hyams 390)
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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
University and elsewhere.
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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
parsing is the “Script Applier Mechanism.”
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• 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.
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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)
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• 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.
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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)
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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.
(Fromkin Rodman Hyams 373-374)
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• 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
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• 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
(Fromkin Rodman Hyams 372)
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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.
(Fromkin Rodman Hyams 403)
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• 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.
(Fromkin Rodman Hyams 403)
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AMBIGUOUS NEWSPAPER HEADLINES
• Red Tape Holds Up New Bridge
• Kids Make Nutritious Snacks
• Sex Education Delayed, Teachers
Request Training
(Fromkin Rodman Hyams 403)
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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
Intelligence.
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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.”
(Fromkin Rodman Hyams 384)
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• “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.”
(Fromkin Rodman Hyams 385)
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•
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:
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SOUND SPECTROGRAM
(Fromkin Rodman Hyams 366)
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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
working in the poem above.
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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?
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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.”
(Fromkin Rodman Hyams 382)
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• “Machine translation is often impeded by
lexical and syntactic ambiguities, structural
disparities between the two languages,
morphological complexities, and other crosslinguistic differences.”
(Fromkin Rodman Hyams 382)
• In the following examples consider what
information must be taken into consideration
for better machine translation:
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• 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.
(Fromkin Rodman Hyams 382)
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Having Fun with
Computer
Terminology
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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.
(Nilsen & Nilsen 98)
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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.
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(Nilsen & Nilsen 99)
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
(Nilsen & Nilsen 167)
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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
(Nilsen & Nilsen 100)
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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.
(Nilsen & Nilsen 99)
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• 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.
(Nilsen & Nilsen 99)
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• 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.
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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.”
(Nilsen & Nilsen 166)
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!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)
‫لووووووووووووووووووووووووووووووووووول‬
OIC: Oh, I see
OMF: Oh, my Gosh
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!!
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
(Nilsen & Nilsen 99)
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!!!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.
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• !!!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.
(Nilsen & Nilsen 177)
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(Eschholz-Rosa-Clark [2009]: 105)
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!!!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-autocorrectfeatured-on-the-tonight-show-with-jay-leno/
THE THE IMPOTENCE OF PROOFREADING (TAYLOR MALI):
http://www.youtube.com/watch?v=p_rwB5_3PQc
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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):
http://www.netlingo.com/top50/popular-text-terms.php
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Related PowerPoints
• Movie Humor
• Stand-Up Comedy
• Television Humor
• Urban Legends (in contrast to Tall Tales
of the Frontier)
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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. “Computational Humor.” IIEEE Intelligent Systems 2006:
59-69.
71
81
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,
Economics, and Life. Chelsea, MI: Sheridan Books, Inc., 2008.
71
82
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.
71
83
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.
71
84
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.
71
85
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.
71
86
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.
71
87
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 HumanComputer Interactional and Computer-Mediated Communication.” HumanComputer 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.
71
88
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.
71
89
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.
71
90
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. “Non-literalness and Nonbona-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. “Current Directions in Computational Humour.”
Artificial Intelligence Review 16.2 (2001): 119-135.
71
91
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.
71
92
Ruch, Willibald, and Paul Ekman. “The Expressive Pattern of
Laughter.” in Emotions, Qualia, and Consciousness, ed. Alfred
Kaszniak. Tokyo, Japan: Word Scientific Publisher, 2001, 426443.
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.”
Information, Communication and Society 10.4 (2007): 465-487.
71
93
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.
71
94
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
Machine.” Communications of the ACM 9.1 (1966): 36-45.
71
95