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COMPUTER LANGUAGE. by Don L. F. Nilsen and Alleen Pace Nilsen. Verbal Literacy vs. Number Literacy. BOTTOM-UP AND TOP-DOWN PROCESSING.

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computer language

COMPUTER LANGUAGE

by Don L. F. Nilsen

and Alleen Pace Nilsen

49

bottom up and top down processing
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)

49

slide4
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.

49

slide5
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.

49

computer words and metaphors
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

49

concordance
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)

49

eliza
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:

49

slide9
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)

49

slide10
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)

49

circuit fix it shop
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?

49

slide12
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)

49

more sophisticated programs
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.

49

sam script applier mechanism
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” devised by Roger Schank and Robert Abelson and modified for humor and language play by Victor Raskin and others.

49

slide15
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.

49

slide16
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)

49

slide17
There are two exciting things about the Script Applier Mechanism. First, it will be 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.

49

parsing problems
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.
  • (Fromkin Rodman Hyams 365, 373)
  • 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)

49

right branching vs embedding
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)

49

slide20
ANOMALOUS WORDS: A sniggle blick is procking a slar.
  • (Fromkin Rodman Hyams 368)
  • METANALYSIS (incorrect phrase breaking):
  • grade A vs. grey day
  • night rate vs. nitrate
  • (Fromkin Rodman Hyams 370)
  • NOTE: English “adder” and “apron” were borrowed incorrectly from the French expressions “un nadder” and “un naperon” respectively

49

slide21
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)

49

real world knowledge
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)

49

slide23
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)

49

ambiguous newspaper headlines
AMBIGUOUS NEWSPAPER HEADLINES
  • Red Tape Holds Up New Bridge
  • Kids Make Nutritious Snacks
  • Sex Education Delayed, Teachers Request Training
  • (Fromkin Rodman Hyams 403)

49

semantic priming
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.

49

speech recognition speech synthesis
SPEECH RECOGNITION & SPEECH SYNTHESIS
  • “Computational phonetics and phonology has two concerns. The first is with programmng 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)

49

slide27
“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)

49

slide28
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:

49

spell checker
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.

49

theories and models
THEORIES AND MODELS
  • In The Physicist’s Conception of Nature, Manfred Eigen said, “A theory has only the alternative 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?

49

translation
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)

49

slide33
“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:

49

slide34
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 guest 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)

49

slide36
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)

49

acronyms
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 are are not obscene.
  • (Nilsen & Nilsen 99)

49

chat groups
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)

49

emoticons
EMOTICONS
  • In conversation we can show our emotions, but on the internet this is difficult, so we use emoticons:
  • :-) Smilling
  • :-)))))))))) Really Smiling
  • ;-) Winking
  • :-* Kissing
  • I-0 Yawning
  • :-& Tongue-Tied
  • :’-{ Crying
  • :-/ Undecided
  • :-II Angry
  • (Nilsen & Nilsen 100)

49

science fiction and fantasy
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 mischievlous 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’ novel.
  • (Nilsen & Nilsen 99)

49

slide41
When someone goes onto the internet to get information that is easily available from the library the Cyber Police might say, “USLT.” 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)

49

slide42
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” which promise customers things that can be delivered or are useless.
  • The “code police” and “net police” are named after the “thought police” in George Orwell’s 1984.

49

signatures
SIGNATURES
  • People like to create enigmatic and puzzing 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)

49

text messaging
TEXT MESSAGING
  • Since numbers and letters require more than a single stroke on cell phones, acronyms are often used:
  • AFAIK: As far as I know
  • BTW: By the way
  • CUL or CUL8R: See you later
  • GIGO: Garbage In Garbage Out
  • GFR: Grime File Reaper
  • LOL: Lots of Laughs
  • OIC: Oh, I see

49

slide45
POS: Parent Over Shoulder
  • ROTF: Rolling on the Floor
  • ROTFLMAO: Rolling on the Floor Laughing My Ass Off
  • RUOK: Are you OK?
  • TIA: Thanks in Advance
  • WYSIWYG: What you See Is What You Get
  • and
  • BCNU: Be Seein’ you
  • (Nilsen & Nilsen 99)

49

twente netherlands
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:

49

slide47
“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-Resoulution 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)

49

virus jokes
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.

49

slide49
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)

49

kurt vonnegut on the internet
!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 said he never wrote it.

49

slide51
!!
  • 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)

49

youtube
!!!YOUTUBE
  • http://youtube.com/watch?v=2Fe110|Miz8

49

slide53
Web Site:

Encyclopedia of 20th Century American Humor. http://www.greenwood.com/catalog/OXHUMOR.aspx

49

slide54
References # 1.

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

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.

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

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.

Hulstijn, J., and A. 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.

49

slide55
References # 2:

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).

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

49

slide56
References # 3:

Raymond, Eric S. The New Hacker’s Dictionary, 2nd Edition. Cambridge, MA: MIT Press, 1993.

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.

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

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

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