COMPUTATIONAL HUMOUR Seminar Presentation Rohan, Avijit, Praveen, Ashutosh, Hemendra

Problem Definition  Modeling verbal humour in a computationally tractable way  Other kinds of humour  Cartoons  Given some keywords  Create a humorous text from it  Problem of recognizing humorous text is a different problem Ashutosh AgarwalSeminar Presentation 2

Outline  Problem Definition  Structure of Common Verbal Jokes  Theories of Humour  Process of Automatic Humour Generation  HAHAcronym system  Conclusion Ashutosh AgarwalSeminar Presentation 3

Hemendra Structure of Common Verbal Jokes 1

One liners  Short sentence with comic effects  Simple syntax, deliberate use of rhetoric devices  Frequent use of creative language constructions  Humor-producing features are guaranteed to be present in the first (and only) sentence.  Suitable for use in an automatic learning setting.  Eg.  Take my advice; I don’t use it anyway.  Beauty is in the eye of the beer holder. HemendraSeminar Presentation 5

Punning Riddles  Question-answer riddle  Uses phonological ambiguity.  Question and Answer in single sentence  Eg.  What do shortsighted ghosts wear? Spooktacles  How do you make gold soup? Put 24 carrots in it HemendraSeminar Presentation 6

Wordplay Jokes  Depend on words that are similar in sound  Used in two different meanings  Difference between the two meanings  creates a conflict  breaks expectation Clifford: The Postmaster General will be making the TOAST. Woody: Wow, imagine a person like that helping out in the kitchen! I shot a elephant in my pajamas. I will always wonder how he got in there. HemendraSeminar Presentation 7

Praveen Theories of Humour 2

Superiority Theory  We laugh about the misfortunes of others  It reflects our own superiority  With such jokes, we are laughing AT someone, not laughing WITH them  Every situation has a winner and a loser  The winner is the one that successfully makes fun of the loser  There’s something about Mary (1998)  Deewane Huye Pagal (2005) PraveenSeminar Presentation 9

Relief Theory  Laughter releases tension & psychic energy  Psychic energy builds up as an aid for suppressing feelings in taboo areas, like sex or death.  When psychic energy is released we experience laughter because  release of psychic energy  Because taboo thoughts are being entertained  Pleasant sensation experienced when humor replaces negative feelings like pain or sadness. PraveenSeminar Presentation 10

Incongruity Theory  Incongruity  Dictionary meaning: “Disagreement of parts”  A joke has two parts : setup & punchline  Setup has 2 meanings  One meaning is most obvious, other meaning remains hidden  Punch line suddenly brings the less obvious meaning in spotlight  This disagreement of setup and punch line is called incongruity PraveenSeminar Presentation 11

Rohan General Theory of Verbal Humour 3

General Theory of Verbal Humour (GTVH)  “How many Poles does it take to screw in a light bulb? Five. One to hold the bulb and four to turn the table he's standing on.” 1. Script opposition 2. Logical mechanism – figure-ground reversal  “How many Poles does it take to screw in a light bulb? Five. One to hold the light bulb and four to look for the right screwdriver” – false analogy RohanSeminar Presentation 13

GTVH – contd. 3. Situation  “How many Poles does it take to wash a car? Two. One to hold the sponge and one to move the car back and forth.” 4. Target 5. Narrative strategy  “It takes five Poles to screw in a light bulb: one to hold the light bulb and four to turn the table he's standing on.” – expository text 6. Language RohanSeminar Presentation 14

Demo

 “You know what’s weird? Donald duck never wore pants… But, whenever he’s getting out of the shower, he always puts a towel around his waist… I mean, what is that about?” - Chandler  Script opposition – dumb vs. non-dumb  Logical mechanism – inconsistency  Situation – shower scene of Donald duck  Target – Disney cartoon character ‘Donald duck’  Narrative strategy – irony  Language – 2 sentences – 2 oppositions RohanSeminar Presentation 17

Avijit Humour Interpretation and Generation 4

Surprise Disambiguation for Jokes  Based on the incongruity resolution theory  Joke consists of a set-up and a punchline  Two interpretations of set-up one more obvious than the other  Punchline creates incongruity  Cognitive rule has to be found out for punchline to follow the set-up naturally AvijitSeminar Presentation 19

Surprise Disambiguation for Jokes Some essential properties  One Obvious interpretation of set-up  Conflict of punchline with obvious set-up  Compatibility of punchline with hidden set-up  Comparison between two set-ups  Inappropriateness of hidden set-up  Another approach : Violation of prediction of set- up AvijitSeminar Presentation 20

Model for Punning Riddles  Syllable substitution  What do shortsighted ghost’s wear? Spooktacles  Word Substitution  How do you make gold soup? Put 24 carrots in it  Metathesis  What is the difference between an oak tree and a tight shoe? One makes acorns, the other makes corns ache AvijitSeminar Presentation 21

Word Substitution  List of homophones already available  Lexicon consists of lexemes and lexical relations  Two requirements: schema and template  Schema : Relations between lexemes  Template: Information to turn schema and lexemes into piece of text  Eg. JAPE (Joke Analysis and Production Engine) AvijitSeminar Presentation 22

Modifying the acronym expansion in a humorous way HAHAcronym 5

Humorous Ironic Acronym Re-analyzer  Resources used  WordNet & WordNet Domains  Synsets tagged with Domain information  Parser, morphological analyzer, etc MIT – Massachusetts Institute of Technology Mythical Institute of Technology ACM-Association of computing machinery Association for confusing machinery AshutoshSeminar Presentation 24

WordNet Domains  250 domain labels  Hierarchy of domains  Opposing semantic fields  On the basis of study of jokes  Examples  Religion Vs Technology  Sex Vs Religion Religion Theology Mythology Art Photography Music Theatre Philosophy Logic Semantics root AshutoshSeminar Presentation 25

Abstract Architecture  Parse the acronym  Choice of what to keep unchanged  What to keep unchanged  Typically it is the head of the NP  Search for possible substitutions  Using semantic field oppositions  WordNet antonymy relations AshutoshSeminar Presentation 26

Evaluation  Human evaluation  Students from universities  70% acronyms were found to be funny  System won Jury’s special prize in a laughter challenge AshutoshSeminar Presentation 27

Conclusion  In this presentation  Humour theories  Humour Generation techniques  Example humour generating system  Humour research is useful for  Designing better human computer interaction systems  Computer aided joke generation Rohan, Praveen, Avijit, Ashutosh & HemendraSeminar Presentation 28

Thank You for your patience ! Questions ? 29

References  F.R.I.E.N.D.S. …  M. Mulder and A. Nijholt, Humour Research : State of the Art, University of Twente, Center for Telematics and Information Technology, Technical Report CTIT , Septeber 2002, 24 pp.  Stock, O. and Strapparava, C HAHAcronym: a computational humor system. In Proceedings of the ACL 2005 on interactive Poster and Demonstration Sessions (Ann Arbor, Michigan, June , 2005). Annual Meeting of the ACL. Association for Computational Linguistics, Morristown, NJ, DOI=  Characterizing Humour: An Exploration of Features in Humorous Texts, Lecture Notes in Computer Science, Springer Berlin / Heidelberg, ISSN: (Print) (Online), Volume 4394/2007, Saturday, May 19, 2007Lecture Notes in Computer Science  

Demo  “You know what’s weird? Donald duck never wore pants… But, whenever he’s getting out of the shower, he always puts a towel around his waist… I mean, what is that about?” - Chandler  Script opposition – dumb vs. non-dumb  Logical mechanism – inconsistency  Situation – shower scene of Donald duck  Target – Disney cartoon character ‘Donald duck’  Narrative strategy – irony  Language – 2 sentences – 2 oppositions RohanSeminar Presentation 31