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Disclaimer : The jokes during the seminar were generated either by AI (Artificial Intelligence) or by AI (Aaditya’s Int

Disclaimer : The jokes during the seminar were generated either by AI (Artificial Intelligence) or by AI (Aaditya’s Intelligence). The bottomline, AI is good. H umour & AI. Aditya M Joshi 08305908 adityaj@cse. Devshree D Sane 08305059 devshreedsane@cse. Under the guidance of

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Disclaimer : The jokes during the seminar were generated either by AI (Artificial Intelligence) or by AI (Aaditya’s Int

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  1. Disclaimer : The jokes during the seminar were generated either by AI (Artificial Intelligence) or by AI (Aaditya’s Intelligence). The bottomline, AI is good.

  2. Humour & AI Aditya M Joshi 08305908 adityaj@cse Devshree D Sane 08305059 devshreedsane@cse Under the guidance of Dr. Pushpak Bhattacharyya

  3. Motivation • Trust • Interpersonal Attraction • Stress Release Why Humour? Why AI? Why Humour & AI? • Use Existing Intelligent • Systems - humans • Model Intelligent systems • as close as possible to them • Computers As Social Actors • Cognitive science studies “Humour is a powerful weapon - you can even break ice with it.”

  4. Scope of the Seminar Humour Generation Humour Recognition

  5. Humour & AI Humour theory Humour theory Computational Humour JAPE-1 HAHAcronym Humour Recognition Applications of Computational humour

  6. What is humour? • Wit • Mirth • Laughter • Manner Components Humour Research Challenges • Humour theory • Sociological Research • Gelotology (Health effects) • Computational humour • Different to different people • Different at different times

  7. Theories of humour • Focus on feelings necessary • for humour. • Mixture of pleasure and pain • at the base of amusement Superiority theory Relief theory Incongruity theory • Focus on effect of humour • Release of nervous energy • Gives a necessary condition • For humour – a ‘twist’. • Humour arises from showing • something absurd from • something that is not. • Based on contradiction of some • sort. Dry humour is a form of humour which is narrated as if it is not a joke at all (i.e. narrated in a serious tone, perhaps.)

  8. Examples of jokes Incongruity theory: "Some people can tell what time it is by looking at the sun.  But I have never been able to make out the numbers." Superiority theory: All the “blonde” or “Sardarji” jokes that are cracked. Relief theory: The “battle-of-the-sexes” jokes A pun in Hindi: Sawaal: Shahrukh Khan ne ek sansthaa ko Rs.10000 ka chandaa diya. Us chande ko kya kehte hain? Jawab: “KHAN-DAAN”. 

  9. Humour & AI Humour theory Computational Humour Computational Humour JAPE-1 HAHAcronym Humour Recognition Applications of Computational humour

  10. What is *computational* humour? • Using computers in humour • research. • Modelling humour in a • computationally tractable way. Definition Areas Our Focus • Humour Generation • Humour Recognition • Out of all forms, • text-based / Verbal Humour • Humour in one-liners

  11. Computational Humour – Linguistic Ambiguity A word is ambiguous if it has more than one meaning. (‘Ambiguous’ is thankfully not ambiguous.  ) • Same sounds, different • meaning. • Three ways: • Syllable substitution • E.g.What do short-sighted ghosts wear? • Spooktacles. • Word substitution • E.g. How do u make gold soup? • Put 14 carrots in it. • Metathesis (Reversal of • sounds) Phonological Morphological Syntactic • Words with samesurface • structure. • E.g. : The book is read / red. • As a result of structure or • syntax of sentence. • Example: “Squad helps dog • bite victims.”

  12. Humour & AI Humour theory Computational Humour JAPE-1 JAPE-1 HAHAcronym Humour Recognition Applications of Computational humour

  13. JAPE-1 • Generates question-answer style puns using phonological similarities • For example, • What do you give an elephant that’s exhausted? • Trunkquillizers.

  14. JAPE-1 : Units • A set of lexemes. • Lexeme is an abstract entity, • roughly corresponding to a • meaning or a phrase. • In addition, a homonym base. Lexicon Schemata Template A set of relationships which must hold between the lexemes To produce the surface form of a joke from the lexemes and relationships specified in an instantiated schema.

  15. JAPE-1: Example Lexeme : jumper Synonym : Sweater Category: Noun Countable: Yes Specifying adjective : Warm Lexicon Schemata Template “What will you get if you cross ____ and ____?” Answer: _______

  16. Humour & AI Humour theory Computational Humour JAPE-1 HAHAcronym HAHAcronym Humour Recognition Applications of Computational humour

  17. HAHAcronym • European project • Humorous Agent for • Humourous Acronyms. • Acronym Ironic • Re-analyzer and generator About HAHAcronym Features Examples • Makes fun of existing • acronyms. • Produces new acronyms • based on concepts provided • by the user. • ACM : • We say: Association for • Computing machinery • HAHA says: Association for • Confusing machinery http://www.haha.itc.it

  18. HAHAcronym : Concepts • group of data elements • that are considered semantically • equivalent for the purposes of • information retrieval. • Eg. Person, Human, Individual Synset WordNet WordNet Domains • Alarge database of English. • Words are grouped into sets • of synonyms (synsets), • each expressing a distinct concept. • Synsets are interlinked by meansof semantic and lexical relations. • Augment WordNet with • domain labels. • Example, the word ‘bank’ has • two labels – • Economy and Geology.

  19. HAHAcronym : Acronym modification Acronym parsing and construction of logical form Choice of what to modify and what to keep unchanged Substitutions 1. Using semantic field oppositions. 2. Reproducing rhyme and rhythm. 3. Adjectives: antonym clustering and semantic relations in WordNet. Recognizes individual constituents such as NP, VP, etc. using acronym grammar.

  20. HAHAcronym : Examples of Acr. Modification • Close Combat Tactical Trainer • Close Combat Theological Trainer • Two changes: antonym strategy • for first adjective and semantic • opposition found in ‘religion' domain • for tactical to theological. CCTT CHI Computer Human Interface. Computer Harry_Truman Interface. Unexpected result: due to rhyming of "human" to "harry_truman"

  21. HAHAcronym : Acronym generation Input: Main concept + Attribute Output: A new funny acronym. Input/Output Example Concept: Writing Attribute: Creative CAUSTIC – Creative Activity for Unconvincingly Sporadically Talkative Individualistic Commercials

  22. Humour & AI Humour theory Computational Humour JAPE-1 HAHAcronym Humour Recognition Humour Recognition Applications of Computational humour

  23. Humour Recognition • Humorous data : One-liners • Non-humorous data : • Sentences from BNC • Sentences from Reuters • Proverbs Experiment Strategies • Stylistic features • Content-based features • Combination of the two

  24. Humour Recognition : Strategies • Antonymy • ( A clean desk is a sign of • cluttered drawer.) • Alliteration • ( Infants don’t enjoy infancy • as much as adults do adultery.) Stylistic Features Content-based features • Naïve Bayes • Uses joint probability of • words and documents.

  25. Humour Recognition : Results

  26. Humour Recognition : N-grams • Naïve Bayes assumes independent words. • N-gram : • Allows a computer to process ‘word sequences’. • Uses conditional probability to predict word based on N-1 previous words. • Allows processing of ‘multi-words’. • Example: wild card, freak out, etc.

  27. Humour & AI Humour theory Computational Humour JAPE-1 HAHAcronym Humour Recognition Applications of Computational humour Applications of Computational humour

  28. Applications • Advertisements • Human-Computer Interaction • Text-based Interface • Embodied Agents (Helpdesks) • Chat bots (A.L.I.C.E.) (Artificial Linguistic Internet Computer entity) PLEASE visit: www.alicebot.org

  29. Applications : ALICE Human: It’s a funny thing! ALICE: I thought it was too. Human: Then why didn’t you laugh? ALICE: Perhaps it is not part of my programming. Human: So you are not programmed to laugh at all? ALICE: Interesting deduction. Ha Ha.

  30. Applications : ALICE Human: Will you marry me? ALICE: Why don’t you just download me?

  31. Conclusion • Humour is a combination of intellect and emotions. It is subject to time, individual experience and perception. • Humour generation produces instances of humour. Textual humour is generated in systems like JAPE and HAHA. • Humour recognition takes help of machine learning techniques to understand the ‘humour’ content of a situation/statement. A conclusion is simply the place where you got tired of thinking.

  32. We learnt… The past was ‘ ’. The future is ‘ ’. computerization humanization

  33. References • Humour Theory and Computational Humour: • www.dcs.gla.ac.uk/~kimb/dai_version/dai_version.html • JAPE-1: • Kim Binsted and Graeme Ritchie. An implemented model of punning riddles. In Twelfth National Conference on Artificial Intelligence (AAAI-94), pages 1-6, 1994. • HAHAcronym: • An Experiment in Automated Humorous Output Production. Oliviero Stock and Carlo Strapparava. In IUT 2003, pages 1-3, 2003. • Humour Recognition: • Making Computers Laugh. Rada Mihalcea and Carlo Strapparava. In Proceedings of HLT/EMNLP, pages 531-538, 2005. • www.wikipedia.org

  34. Humour & AI Questions? Comments? Suggestions? The past was computerization. The future is humanization. 

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