Computers and Humor
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.
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.”
• “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
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?
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
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.
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
•
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
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,
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
•
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
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.
•
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,
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.”
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
•
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.
• 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)
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)
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)
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
• 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
•
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.
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
•
!!!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
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