CHAPTER%209%20LANGUAGE%20PROCESSING:%20HUMANS%20AND%20COMPUTERS

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CHAPTER 9LANGUAGE PROCESSINGHUMANS AND
COMPUTERS

• PowerPoint by Don L. F. Nilsen
• to accompany
• An Introduction to Language (8th or 9th edition,
  2007/2011)
• by Victoria Fromkin, Robert Rodman
• and Nina Hyams

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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 2011 381-382)

3

• 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 womens minds have vacant
  chambers that mens minds dont 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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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,
  Sorcerers 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 2011 407-409)

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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 Theyre 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 Im depressed much of the time.
• ELIZA I am sorry to hear that you are depressed.
• (Fromkin Rodman Hyams 2011 398)

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• Victor Raskin fooled ELIZA by saying, I dont
  like my mother and I dont 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 2011 398-402, 405)

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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 devised
  by Roger Schank and Robert Abelson and modified
  for humor and language play by Victor Raskin and
  others.

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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 will be able to
  spot anything that is missing, added, or out of
  place in the sequence of events and ask, Whats
  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.
• (Fromkin Rodman Hyams 2011 385)
• EMBEDDING Never imagine yourself not to be
  otherwise than what it might appear to othersto
  be otherwise.
• (Lewis Carrolls Alices Adventures in
  Wonderland)
• (Fromkin Rodman Hyams 2011 377)

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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 2011 386)

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• ANOMALOUS WORDS A sniggle blick is procking a
  slar.
• (Fromkin Rodman Hyams 2007 368)
• METANALYSIS (incorrect phrase breaking)
• grade A vs. grey day
• night rate vs. nitrate
• (Fromkin Rodman Hyams 2007 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 2011 384)

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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 dont know it, we have a nursery
  downstairs.
• NEWSPAPER AD Our bikinis are exciting they are
  simply the tops.
• (Fromkin Rodman Hyams 2011 423-424)

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• ANTISMOKING CAMPAIGN SLOGAN
• Its 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 2011 423-424)

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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 2011 423-424)

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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 2011 383-384)
• 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 2011 391-395)

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• Machines which imitate human speech, are so
  difficult to construct that many agencies are
  involved in producing a single word.
• Things that must be considered include not only
  the sounds, but also the inflections and
  variations of tone and articulation.
• (Fromkin Rodman Hyams 2011 391-395)

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• TO SYNTHESIZE SPEECH
• 1. Start with a tone at the same frequency as
  vibrating vocal cords (higher if a womans or
  childs voice is being synthesized, lower for a
  mans)
• 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 2011 394)
• 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 2011
379)
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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 2011 411)
• Explain why the spell checker is not working in
  the poem above.

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THEORIES AND MODELS

• In The Physicists 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 2007 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 not just a 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 2011 391-406)

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• 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 2011 391-406)
• 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 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 2007 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 megabyte.
• (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 are are not obscene.
• (Nilsen Nilsen 99)

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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 mischievlous program is called a wabbit from
  Elmer Fudds You wascawwy wabbit.
• A program that repeats itself indefinitely is
  said to be in Sorcerers Apprentice Mode from
  Fantasia
• The meaning of life, truth, and everything is
  42 from a computer in Douglas Adams novel.
• (Nilsen Nilsen 99)

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• When someone goes onto the internet to get
  information that is easily available from a
  manual, etc. 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)

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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 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 Orwells
  1984.

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SIGNATURES

• People like to create enigmatic and puzzling
  signatures. One user named Eddie follows his
  signature with Ceci nest pas une signature.
• This is an allusion to a painting of a pipe by
  René Magritte with the disclaimer, Ceci nest
  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
• 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

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• OMG Oh My Gosh
• http//www.youtube.com/watch?v0P0jY-Di6fg
• 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
• WTF Not translatable
• WYSIWYG What you See Is What You Get
• BCNU Be Seein you
• (Nilsen Nilsen 99)

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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-Resoulutio
  n Theory of Humor
• Humorous Analogy Modeling the Devils
  Dictionary.
• Why Is a Riddle Not Like a Metaphor? and

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VIRUS JOKES

• ATT Virus Every three minutes it tells you
  what great service you are getting.
• MCI Virus Every three minutes it reminds you
  that youre paying too much for the ATT virus.

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• Paul Revere Virus This revolutionary virus does
  not horse around. It warns you of impending hard
  disk attackonce if by LAN, twice if by Cgt.
• 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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!KURT VONNEGUT ON THE INTERNET

• In August of 1997 a piece appeared on the
  Internet by Kurt Vonnegut.
• When Vonneguts 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.

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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 Vonneguts,
  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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!!!Computer-Humor Websites

• ANIMATOR VS. ANIMATION II
• http//www.metacafe.com/watch/689540/animator_vs_a
  nimation_2/
• THE THE IMPOTENCE OF PROOFREADING (TAYLOR MALI)
• http//www.youtube.com/watch?vp_rwB5_3PQc
• TOP 50 POPULAR TEXT CHAT ACRONYMS (NETLINGO)
• http//www.netlingo.com/top50/popular-text-terms.p
  hp
• USHERS OMG, FEATURING WILL.I.AMAUTOTUNE
• http//www.youtube.com/watch?v0P0jY-Di6fg

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• Hulstijn, J., and A. Nijholt eds. Twente Workshop
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