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  1. 1. Computers and Humor by Don L. F. Nilsen and Alleen Pace Nilsen
  2. 2. Bill Gates
  3. 3. Computer Generated Humor: Apple’s Joke Teller <ul><li>Given the command, “Computer, tell me a joke,” this is one response: </li></ul><ul><li>COMPUTER: Knock, knock. </li></ul><ul><li>YOU: Who’s there. </li></ul><ul><li>COMPUTER: Thistle. </li></ul><ul><li>YOU: Thistle who? </li></ul><ul><li>COMPUTER: “Thistle be my last knock knock joke. (Hemplemann, 333) </li></ul>
  4. 4. Natural Language Processing: Suspension of Disbelief <ul><li>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). </li></ul>
  5. 5. Computers with a Sense of Humor <ul><li>Kim Binstead says that humor can help “make clarification queries less repetitive, statements of ignorance more acceptable, and error messages less patronizing.” (Hempelmann 336) </li></ul><ul><li>John Morkes et. al. demonstrate that computer systems that employ humor are viewed as “more likable and competent” (Morkes 215). </li></ul>
  6. 6. FACS: Facial Action Coding System <ul><li>“ Based on an anatomical analysis of facial action, FACS describes facial expressions and movements and in a second step relates them to emotions.” </li></ul><ul><li>FACS distinguishes between different types of smiles and laughs by using such parameters as frequency, intensity, duration, and symmetry. </li></ul><ul><li>Paul Ekman and Wallace Friesen are using the FACS to build gestural facial and bodily expressions into computer programs. </li></ul><ul><li>FACS has also been used by the movie industry in such films as Shrek and Toy Story . </li></ul><ul><li>(Hempelmann 337) </li></ul>
  7. 7. JAPE: Joke Analysis and Production Engine <ul><li>Kim Binstead and Graeme Ritchie are using the JAPE system to generate humor. </li></ul><ul><li>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. </li></ul><ul><li>(Hempelmann 337) </li></ul>
  8. 8. A JAPE Joke <ul><li>JAPE would use information like the following to produce this joke: </li></ul><ul><li>(i) “cereal” IS-A “breakfast food” </li></ul><ul><li>(ii) “murderer” IS-A “killer” </li></ul><ul><li>(iii) “cereal” SOUNDS-LIKE “serial” </li></ul><ul><li>(iv) “serial klller” is a meaningful phrase </li></ul><ul><li>Q: What do you get when you cross a breakfast food with a murderer? </li></ul><ul><li>A: A cereal killer. </li></ul><ul><li>(Hempelmann 338) </li></ul>
  9. 9. STANDUP: Interactive Riddle Builder <ul><li>STANDUP has a larger resource size than JAPE. </li></ul><ul><li>STANDUP is designed to help children with language problems stay on task. </li></ul><ul><li>Children use the STANDUP program to produce riddles, and the humor in the program keeps the children interested and active. </li></ul><ul><li>But STANDUP has basically the same level of computer sophistication as does JAPE. </li></ul><ul><li>(Hempelmann 340) </li></ul>
  10. 10. How to Make a Computer Laugh: Computer Recognition of One-Liners <ul><li>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. </li></ul><ul><li>(Hempelmann 340) </li></ul>
  11. 11. Humorous Signals Human Centeredness & Polarity Orientation <ul><li>The expressions that correlate with humor can be categorized as: </li></ul><ul><li>Human-Centric Vocabulary (pronouns…) </li></ul><ul><li>Negative Evaluations (“wrong,” “error”…) </li></ul><ul><li>Professional Communities (“lawyers,” “programmers”…) </li></ul><ul><li>Negative Traits (“ignorance,” “lying”…) </li></ul><ul><li>(Hempelmann 340) </li></ul>
  12. 12. Fuzzy Logic <ul><li>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.” </li></ul><ul><li>Identifying the ambiguity is relatively easy, but deciding which ambiguity is humorous is much more difficult. </li></ul><ul><li>(Hempelmann 341) </li></ul>
  13. 13. EIGENTASTE  JESTER <ul><li>Eigentaste is a “constant time collaborative filtering algorithm.” </li></ul><ul><li>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. </li></ul><ul><li>(Hempelmann 341). </li></ul>
  14. 14. GTVH: General Theory of Verbal Humor LIBJOG: Lightbulb-Joke Generator <ul><li>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.” </li></ul><ul><li>“ 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.” </li></ul><ul><li>(Hempelmann 338) </li></ul>
  15. 15. SSTH: Semantic Script Theory of Humor and the HAHAcronym Generator <ul><li>The HAHAcronym Generator is loosely based on Raskin and Attardo’s SSTH. </li></ul><ul><li>“ 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.” </li></ul><ul><li>“ MIT becomes “Mythical Institute of Theology.” </li></ul><ul><li>(Hempelmann 339) </li></ul>
  16. 16. SSTH: Semantic Script Theory of Humor: <ul><li>SSTH shows script overlap and script oppositeness. </li></ul><ul><li>“ 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.” </li></ul><ul><li>(Hempelmann 342) </li></ul>
  17. 17. SSTH and Ontological Semantics <ul><li>For the Semantic Script Theory of Humor to be really effective, it must include ontological semantics. </li></ul><ul><li>But ontological semantics needs to systematically deal with the information found in dictionaries, encyclopedias, thesauruses, and many other types of reference books. </li></ul><ul><li>(Hempelmann 347) </li></ul>
  18. 18. Using Ontological Semantics to Generate a Joke <ul><li>In his “Computational Humor: Beyond the Pun?” Christian Hempelmann gives seven pages of rigorous and systematic details to generate the following joke: </li></ul><ul><li>Q: What did the egg say in the monastery? </li></ul><ul><li>A: Out of the frying pan, into the friar. </li></ul>
  19. 19. Joke vs. Wordplay <ul><li>For people who fail to see the overlap in a joke, it isn’t a joke at all. It is merely word play. </li></ul><ul><li>“ 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.” </li></ul><ul><li>(Hempelmann 346) </li></ul>
  20. 20. Klangspiel: Play with Sounds, vs. Sinnspiel: Play with Meanings <ul><li>“ 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.” </li></ul><ul><li>“ 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.” </li></ul><ul><li>(Hempelmann 346). </li></ul>
  21. 21. Ynperfect Pun Selector <ul><li>In an article entitled, “Ynperfect Pun Selector for Computational Humor,” Christian Hempelmann gives the following joke: </li></ul><ul><li>A. Knock knock. B. Who’s there? </li></ul><ul><li>A. Cantaloupe. B. Cantaloupe who? </li></ul><ul><li>A. Can’t elope tonight—Dad’s got the car. </li></ul><ul><li>Hempelmann also considered bilingual punning, as in, “Those who jump off a Paris bridge are in Seine” (Hempelmann 342-343). </li></ul>
  22. 22. Willing Suspension of Disbelief in A Joke <ul><li>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.” </li></ul><ul><li>Hempelmann considers a joke, as an aesthetic text, to be a specific type of poetry. But the joke also requires opposition and incongruity. </li></ul><ul><li>Willing suspension of disbelief is required “to reconcile this incongruity and at least playfully, make it spuriously appropriate.” </li></ul><ul><li>Note that this same willing suspension of disbelief is required in religion and in magic (Hempelmann 344-345). </li></ul>
  23. 23. Verbal Literacy vs. Number Literacy
  24. 24. BOTTOM-UP AND TOP-DOWN PROCESSING <ul><li>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. </li></ul><ul><li>Top-down processing is based on reasoning. You make a generalization and see how well the sounds, letters, morphemes, etc. support your generalization. </li></ul><ul><li>(Fromkin Rodman Hyams 369) </li></ul>
  25. 25. <ul><li>Top-down reasoning is powerful, but it can be dangerous if it is not accompanied by bottom-up reasoning. </li></ul><ul><li>For example, Otto Jesperson assumed that men were better thinkers than women. </li></ul><ul><li>He conducted an experiment in which men and women read a story and were given a quiz. </li></ul>
  26. 26. <ul><li>The women responded more quickly and more accurately than the men, which was not what Jacobson had expected. </li></ul><ul><li>So he concluded that women’s minds have “vacant chambers” that men’s minds don’t have. </li></ul><ul><li>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. </li></ul>
  27. 27. Boolean Algebra <ul><li>Christie Davies says Boolean algebra “enables users to hide problems and assumptions behind algebraic symbols. </li></ul><ul><li>You can not easily turn words into numbers. </li></ul><ul><li>Those who try to do so usually do not understand either.” </li></ul><ul><li>(Davies [2008]: 178) </li></ul>
  28. 28. COMPUTER WORDS AND METAPHORS <ul><li>COMPUTER WORDS: bits, bytes, code police, cyberspace, future shock, hackers, hard copy, menu, third wave, user-friendly </li></ul><ul><li>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 </li></ul>
  29. 29. CONCORDANCE <ul><li>A Concordance checks for word frequency and word associations. </li></ul><ul><li>Word associations are determined by providing a window of three words (more or less) on each side of the targeted word. </li></ul><ul><li>(Fromkin Rodman Hyams 379) </li></ul>
  30. 30. ELIZA <ul><li>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. </li></ul><ul><li>ELIZA claims to be a psychiatrist talking to a patient. ELIZA, like a psychiatrist, is attempting to get rather than give information: </li></ul>
  31. 31. <ul><li>PATIENT: Men are all alike. </li></ul><ul><li>ELIZA: In what way? </li></ul><ul><li>PATIENT: They’re always bugging me about something or other. </li></ul><ul><li>ELIZA: Can you think of a specific example? </li></ul><ul><li>PATIENT: Well, my boyfriend made me come here. </li></ul><ul><li>ELIZA: Your boyfriend made you come here? </li></ul><ul><li>PATIENT: He says I’m depressed much of the time. </li></ul><ul><li>ELIZA: I am sorry to hear that you are depressed. </li></ul><ul><li>(Fromkin Rodman Hyams 389) </li></ul>
  32. 32. <ul><li>Victor Raskin fooled ELIZA by saying, “I don’t like my mother and I don’t want to talk about her.” </li></ul><ul><li>ELIZA responded, “Tell me more about your mother. </li></ul><ul><li>(Nilsen Nilsen & Combs 199) </li></ul>
  33. 33. CIRCUIT FIX-IT SHOP <ul><li>Twenty-five years after ELIZA, a program named “Circuit Fix-It Shop” was developed: </li></ul><ul><li>COMPUTER: This is the circuit fix-it shop. How may I help you? </li></ul><ul><li>TECHNICIAN: I need to fix the RS111 circuit. </li></ul><ul><li>COMPUTER: I am familiar with that circuit. The LED is supposed to be displaying alternatively blinking one and seven. </li></ul><ul><li>TECHNICIAN: Where is the LED? </li></ul>
  34. 34. <ul><li>COMPUTER: In the middle right of the blue region is the LED. </li></ul><ul><li>TECHNICIAN: No light. </li></ul><ul><li>COMPUTER: What is the switch at when the LED is off? </li></ul><ul><li>TECHNICIAN: The switch is down. </li></ul><ul><li>COMPUTER: You put up the switch. </li></ul><ul><li>(Fromkin Rodman Hyams 390) </li></ul>
  35. 35. MORE SOPHISTICATED PROGRAMS <ul><li>Today more sophisticated programs are needed. One such program is the little Paperclip guy that answers questions in Microsoft Word. </li></ul><ul><li>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. </li></ul>
  36. 36. SAM: SCRIPT APPLIER MECHANISM <ul><li>Of course sentences need to be parsed in Artificial Intelligence. But constituents larger than a sentence must be parsed as well. </li></ul><ul><li>One of the devices for doing this discourse parsing is the “Script Applier Mechanism.” </li></ul>
  37. 37. <ul><li>Note that a play or a movie has a script for the actors to follow. </li></ul><ul><li>The script in Artificial Intelligence is the same, but it is much simpler. It is a “mundane script.” </li></ul><ul><li>The “Restaurant Script,” for example involves a customer, a server, a cashier, etc. </li></ul>
  38. 38. <ul><li>Props in the “Restaurant Script” include the restaurant, the table, the menu, the food, the check, the payment, the tip, etc. </li></ul><ul><li>The sequence of actions is as follows: </li></ul><ul><li>1. Customer goes to restaurant. </li></ul><ul><li>2. Customer goes to table. </li></ul><ul><li>3. Server brings menu. </li></ul><ul><li>4. Customer orders food. </li></ul><ul><li>5. Server brings food. </li></ul><ul><li>6. Customer eats food. </li></ul><ul><li>7. Server brings check. </li></ul><ul><li>8. Customer leaves tip for server. </li></ul><ul><li>9. Customer gives payment to cashier. </li></ul><ul><li>10. Customer leaves restaurant. </li></ul><ul><li>(Hendrix and Sacerdote 654) </li></ul><ul><li>(Nilsen Nilsen & Combs 199) </li></ul>
  39. 39. <ul><li>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.” </li></ul><ul><li>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. </li></ul>
  40. 40. PARSING PROBLEMS <ul><li>GARDEN PATH: </li></ul><ul><li>The horse raced past the barn fell. </li></ul><ul><li>After the child visited the doctor prescribed a course of injections. </li></ul><ul><li>The doctor said the patient will die yesterday. </li></ul><ul><li>EMBEDDING: “Never imagine yourself not to be otherwise than what it might appear to others…to be otherwise.” </li></ul><ul><li>(Lewis Carroll’s Alice’s Adventures in Wonderland ) </li></ul><ul><li>(Fromkin Rodman Hyams 365, 373) </li></ul>
  41. 41. RIGHT-BRANCHING VS. EMBEDDING <ul><li>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. </li></ul><ul><li>EMBEDDING: Jack built the house that the malt that the rat that the cat that the dog worried killed ate lay in. </li></ul><ul><li>NOTE Multiple embedding is OK for a computer, but not OK for the human brain. </li></ul><ul><li>(Fromkin Rodman Hyams 373-374) </li></ul>
  42. 42. <ul><li>ANOMALOUS WORDS: A sniggle blick is procking a slar. </li></ul><ul><li>METANALYSIS (incorrect phrase breaking): </li></ul><ul><li>grade A vs. grey day </li></ul><ul><li>night rate vs. nitrate </li></ul><ul><li>(Fromkin Rodman Hyams 368, 370) </li></ul><ul><li>NOTE: English “adder” and “apron” were borrowed incorrectly from the French expressions “un nadder” and “un naperon” respectively </li></ul>
  43. 43. <ul><li>AMBIGUOUS SYNTAX IN NEWSPAPER HEADLINES: </li></ul><ul><li>Teacher Strikes Idle Kids </li></ul><ul><li>Enraged Cow Injures Farmer with Ax </li></ul><ul><li>Killer Sentenced to Die for Second Time in 10 Years </li></ul><ul><li>Stolen Painting Found by Tree </li></ul><ul><li>(Fromkin Rodman Hyams 372) </li></ul>
  44. 44. REAL-WORLD KNOWLEDGE <ul><li>Explain why the following sentences are ambiguous to a computer but not to a human: </li></ul><ul><li>A cheesecake was on the table. It was delicious and was soon eaten. </li></ul><ul><li>SIGN IN A CHURCH: For those of you who have children and don’t know it, we have a nursery downstairs. </li></ul><ul><li>NEWSPAPER AD: Our bikinis are exciting; they are simply the tops. </li></ul><ul><li>(Fromkin Rodman Hyams 403) </li></ul>
  45. 45. <ul><li>ANTISMOKING CAMPAIGN SLOGAN: It’s time we make smoking history. </li></ul><ul><li>Do you know the time? </li></ul><ul><li>Concerned with spreading violence, the president called a press conference. </li></ul><ul><li>The ladies of the church have cast off clothing of every kind and they may be seen in the church basement Friday. </li></ul><ul><li>(Fromkin Rodman Hyams 403) </li></ul>
  46. 46. AMBIGUOUS NEWSPAPER HEADLINES <ul><li>Red Tape Holds Up New Bridge </li></ul><ul><li>Kids Make Nutritious Snacks </li></ul><ul><li>Sex Education Delayed, Teachers Request Training </li></ul><ul><li>(Fromkin Rodman Hyams 403) </li></ul>
  47. 47. SEMANTIC PRIMING <ul><li>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.” </li></ul><ul><li>This is because “doctor” and “nurse” “are located in the same part of the mental lexicon.” </li></ul><ul><li>(Fromkin Rodman Hyams 371) </li></ul><ul><li>This same feature could easily be built into Artificial Intelligence. </li></ul>
  48. 48. SPEECH RECOGNITION & SPEECH SYNTHESIS <ul><li>“ Computational phonetics and phonology has two concerns. The first is with programming computers to analyze the speech signal into its component phones and phonemes. </li></ul><ul><li>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. </li></ul><ul><li>The first of these is speech recognition ; the second is speech synthesis .” </li></ul><ul><li>(Fromkin Rodman Hyams 384) </li></ul>
  49. 49. <ul><li>“ 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.” </li></ul><ul><li>(Fromkin Rodman Hyams 385) </li></ul>
  50. 50. <ul><li>TO SYNTHESIZE SPEECH: </li></ul><ul><li>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) </li></ul><ul><li>2. Emphasize the harmonics corresponding to the formants required for a particular vowel, liquid, or nasal quality. </li></ul><ul><li>3. Add hissing or buzzing for fricatives. </li></ul><ul><li>4. Add nasal resonances for nasal sounds. </li></ul><ul><li>5. Temporarily cut off sound to produce stops and affricates…. </li></ul><ul><li>(Fromkin Rodman Hyams 386) </li></ul><ul><li>A Sound Spectrogram will give an indication of some of the variables of analyzing or synthesizing speech: </li></ul>
  51. 51. SOUND SPECTROGRAM (Fromkin Rodman Hyams 366)
  52. 52. SPELL CHECKER <ul><li>I have a spelling checker. </li></ul><ul><li>It came with my PC. </li></ul><ul><li>It plane lee marks four my revue </li></ul><ul><li>Miss steaks aye can knot sea. </li></ul><ul><li>(Fromkin Rodman Hyams 381) </li></ul><ul><li>Explain why the spell checker is not working in the poem above. </li></ul>
  53. 53. THEORIES AND MODELS <ul><li>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.” </li></ul><ul><li>(Fromkin Rodman Hyams 397) </li></ul><ul><li>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? </li></ul>
  54. 54. TRANSLATION <ul><li>“ 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.” </li></ul><ul><li>(Fromkin Rodman Hyams 382) </li></ul>
  55. 55. <ul><li>“ Machine translation is often impeded by lexical and syntactic ambiguities, structural disparities between the two languages, morphological complexities, and other cross-linguistic differences.” </li></ul><ul><li>(Fromkin Rodman Hyams 382) </li></ul><ul><li>In the following examples consider what information must be taken into consideration for better machine translation: </li></ul>
  56. 56. <ul><li>BUCHAREST HOTEL: The lift is being fixed for the next day. During that time we regret that you will be unbearable. </li></ul><ul><li>SWISS NUNNERY HOSPITAL: The nuns harbor all diseases and have no respect for religion. </li></ul><ul><li>GERMAN HOTEL: All the water has been passed by the manager. </li></ul><ul><li>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. </li></ul><ul><li>TURKEY: The government bans the smoking of children. </li></ul><ul><li>(Fromkin Rodman Hyams 382) </li></ul>
  57. 57. <ul><li>Having Fun with Computer Terminology </li></ul>
  58. 58. 1024 <ul><li>When Alan Schoenfeld of the University of California at Berkeley attended a conference on Artificial Intelligence, he was given Hotel Room Number 1024. </li></ul><ul><li>Wow! he said. </li></ul><ul><li>1024 is 2 to the tenth power. It is a kilobyte. </li></ul><ul><li>(Nilsen & Nilsen 98) </li></ul>
  59. 59. ACRONYMS <ul><li>Acronyms are so common in computer terminology that programmers make fun of them. </li></ul><ul><li>“ TLA” stands for “Three Letter Acronym.” </li></ul><ul><li>“ YABA” stands for “Yet Another Bloody Acronym.” </li></ul><ul><li>“ YABA Compatible” means that the initials can be pronounced easily, and are not ambiguous or offensive. </li></ul><ul><li>(Nilsen & Nilsen 99) </li></ul>
  60. 60. CHAT GROUPS <ul><li>Linguist Susan Herring at the University of Texas, Arlington studied the humor in chat groups. Her results were as follows: </li></ul><ul><li>imaginary situations: 20 percent </li></ul><ul><li>a mock persona: 14 percent </li></ul><ul><li>teasing: 13 percent </li></ul><ul><li>irony: 6 percent </li></ul><ul><li>name play: 5 percent </li></ul><ul><li>silliness: 4 percent </li></ul><ul><li>real situations: 3 percent </li></ul><ul><li>riddles: 2 percent </li></ul><ul><li>pretended misunderstandings: 2 percent </li></ul><ul><li>puns: 1 percent </li></ul><ul><li>(Nilsen & Nilsen 167) </li></ul>
  61. 61. EMOTICONS <ul><li>In conversation we can show our emotions, but on the internet this is difficult, so we use emoticons: </li></ul><ul><li>:-) Smiling </li></ul><ul><li>:-)))))))))) Really Smiling </li></ul><ul><li>;-) Winking </li></ul><ul><li>:-* Kissing </li></ul><ul><li>I-0 Yawning </li></ul><ul><li>:-& Tongue-Tied </li></ul><ul><li>:’-{ Crying </li></ul><ul><li>:-/ Undecided </li></ul><ul><li>:-II Angry </li></ul><ul><li>(Nilsen & Nilsen 100) </li></ul>
  62. 62. SCIENCE FICTION AND FANTASY <ul><li>Many computer terms come from Science Fiction and Fantasy: </li></ul><ul><li>A huge network packet is a “Godzillagram” from Godzilla </li></ul><ul><li>Teenage hackers are “Munchkins” from The Wizard of Oz </li></ul><ul><li>A mischievious program is called a “wabbit” from Elmer Fudd’s “You wascawwy wabbit.” </li></ul><ul><li>A program that repeats itself indefinitely is said to be in “Sorcerer’s Apprentice Mode” from Fantasia </li></ul><ul><li>The meaning of life, truth, and everything is “42” from a computer in Douglas Adams’ Hitchhiker’s Guide to the Galaxy . </li></ul><ul><li>(Nilsen & Nilsen 99) </li></ul>
  63. 63. <ul><li>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 . </li></ul><ul><li>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. </li></ul><ul><li>(Nilsen & Nilsen 99) </li></ul>
  64. 64. <ul><li>In computer terminology a soft boot refers to the hitting of “Control,” “Alternate” and “Delete” at the same time. </li></ul><ul><li>This is refered to as the “Vulcan Nerve Pinch” from Star Trek . </li></ul><ul><li>“ 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. </li></ul><ul><li>The “code police” and “net police” are named after the “thought police” in George Orwell’s 1984 . </li></ul>
  65. 65. SIGNATURES <ul><li>People like to create enigmatic and puzzling signatures. One user named Eddie follows his signature with “Ceci n’est pas une signature.” </li></ul><ul><li>This is an allusion to a painting of a pipe by René Magritte with the disclaimer, “Ceci n’est pas une pipe.” </li></ul><ul><li>(Nilsen & Nilsen 166) </li></ul>
  66. 66. TEXT MESSAGING <ul><li>Since numbers and letters require more than a single stroke on cell phones, acronyms are often used: </li></ul><ul><li>AFAIK: As far as I know </li></ul><ul><li>BTW: By the way </li></ul><ul><li>CUL or CUL8R: See you later </li></ul><ul><li>GIGO: Garbage In Garbage Out </li></ul><ul><li>GFR: Grim File Reaper </li></ul><ul><li>LOL: Lots of Laughs </li></ul><ul><li>OIC: Oh, I see </li></ul>
  67. 67. <ul><li>POS: Parent Over Shoulder </li></ul><ul><li>ROTF: Rolling on the Floor </li></ul><ul><li>ROTFLMAO: Rolling on the Floor Laughing My Ass Off </li></ul><ul><li>RUOK: Are you OK? </li></ul><ul><li>TIA: Thanks in Advance </li></ul><ul><li>WYSIWYG: What you See Is What You Get </li></ul><ul><li>and </li></ul><ul><li>BCNU: Be Seein’ you </li></ul><ul><li>(Nilsen & Nilsen 99) </li></ul>
  68. 68. TWENTE, NETHERLANDS <ul><ul><li>Every year there is an annual workshop on Language Technology at the University of Twente. </li></ul></ul><ul><ul><li>In 1996 this workshop was devoted to “Automatic Interpretation and Generation of Verbal Humor.” </li></ul></ul><ul><ul><li>The papers at this conference had such titles as: </li></ul></ul>
  69. 69. <ul><li>“ Why do People Use Irony?” </li></ul><ul><li>“ Password Swordfish: Verbal Humour in the Interface.” </li></ul><ul><li>“ Computer Implementation of the General Theory of Verbal Humor.” </li></ul><ul><li>“ Humor Theory beyond Jokes.” </li></ul><ul><li>“ Speculations on Story Puns.” </li></ul><ul><li>“ Relevance Theory and Humorous Interpretations.” </li></ul><ul><li>“ What Sort of a Speech Act is the Joke?” </li></ul><ul><li>“ A Neural Resolution of the Incongruity-Resolution Theory of Humor” </li></ul><ul><li>“ Humorous Analogy: Modeling the Devil’s Dictionary .” </li></ul><ul><li>“ Why Is a Riddle Not Like a Metaphor?” and </li></ul><ul><li>“ An Attempt at Natural Humor from a Natural Language Robot.” </li></ul><ul><li>(Nilsen and Nilsen 98) </li></ul>
  70. 70. VIRUS JOKES <ul><li>AT&T Virus: Every three minutes it tells you what great service you are getting. </li></ul><ul><li>MCI Virus: Every three minutes it reminds you that you’re paying too much for the AT&T virus. </li></ul>
  71. 71. <ul><li>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:>. </li></ul><ul><li>New World Order Virus: Probably harmless, but it makes a lot of people really mad just thinking about it. </li></ul><ul><li>(Nilsen & Nilsen 177) </li></ul>
  72. 72. !KURT VONNEGUT ON THE INTERNET <ul><li>In August of 1997 a piece appeared on the Internet by Kurt Vonnegut. </li></ul><ul><li>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. </li></ul><ul><li>Vonnegut said that it was “funny and wise and charming,” but he never wrote it. </li></ul>
  73. 73. <ul><li>!! </li></ul><ul><li>The article had actually been published by Mary Schmich in the Chicago Tribune and then picked up and redistributed by a computer hacker. </li></ul><ul><li>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. </li></ul><ul><li>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. </li></ul><ul><li>Probably both opinions are true. </li></ul><ul><li>(Nilsen & Nilsen 168) </li></ul>
  74. 74. (Eschholz-Rosa-Clark [2009]: 105)
  75. 75. !!!Computer Humor Website <ul><li>ANIMATOR VS. ANIMATION II: </li></ul><ul><li>http://www.metacafe.com/watch/689540/animator_vs_animation_2/ </li></ul><ul><li>THE THE IMPOTENCE OF PROOFREADING (TAYLOR MALI): </li></ul><ul><li>http://www.youtube.com/watch?v=p_rwB5_3PQc </li></ul><ul><li>TOP 50 POPULAR TEXT & CHAT ACRONYMS (NETLINGO): </li></ul><ul><li>http://www.netlingo.com/top50/popular-text-terms.php </li></ul>
  76. 76. Related PowerPoints <ul><li>Movie Humor </li></ul><ul><li>Stand-Up Comedy </li></ul><ul><li>Television Humor </li></ul><ul><li>Urban Legends (in contrast to Tall Tales of the Frontier) </li></ul>
  77. 77. <ul><li>References: </li></ul><ul><li>Attardo, Salvatore. Humorous Texts: A Semantic and Pragmatic Analysis . New York, NY: Mouton de Gruyter, 2001. </li></ul><ul><li>Attardo, Salvatore. Linguistic Theory of Humor . New York, NY: Mouton de Gruyter, 1994. </li></ul><ul><li>Attardo, Salvatore. “The Semantic Foundations of Cognitive Theories of Humor.” HUMOR: International Journal of Humor Research 10.4 (1997): 395-420. </li></ul><ul><li>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. </li></ul><ul><li>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. </li></ul>
  78. 78. <ul><li>Binstead, Kim. Using Humour to Make Natural Language Interfaces More Friendly . Unpublished MS Thesis, Edinburgh, Scotland: University of Edinburgh, 1995. </li></ul><ul><li>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. </li></ul><ul><li>Binstead, Kim, and Graeme Ritchie. “An Implemented Model of Punning Riddles.” in Stock (2002): 633-638. </li></ul><ul><li>Binstead, Kim, and Graeme Ritchie. “Computational Rules for Generating Punning Riddles.” HUMOR: International Journal of Humor Research 10.1 (1997): 25-76. </li></ul><ul><li>Carrell, Amy. “Joke Competence and Humor Competence.” HUMOR: International Journal of Humor Research 10.2 (1997): 173-185. </li></ul>
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