Disclaimer :

The jokes during the seminar

were generated either by AI (Artificial Intelligence)

or by AI (Aaditya’s Intelligence).

The bottomline, AI is good.

Humour & AI

Devshree D Sane

08305059

devshreedsane@cse

Aditya M Joshi

08305908

adityaj@cse

Under the guidance of

Dr. Pushpak Bhattacharyya

Why Humour?

Why AI?

Why Humour & AI?

• Trust

• Interpersonal Attraction

• Stress Release

• Use Existing Intelligent Systems - humans

• Model Intelligent systems as close as possible to them

Motivation

• Computers As Social Actors• Cognitive science studies

“Humour is a powerful weapon - you can even break ice with it.”

Scope of the Seminar

Humour Generation

Humour Recognition

Humour & AI

Humour theoryComputational

HumourJAPE-1 HAHAcronym

Humour Recognition

Applications of Computational

humour

Humour theory

Components

Humour Research

Challenges

• Wit

• Mirth

• Laughter

• Manner

• Humour theory• Sociological Research• Gelotology (Health effects)• Computational humour

What is humour?

• Different to different people• Different at different times

Superiority theory

Relief theory

Incongruity theory•Focus on feelings necessaryfor humour.

• Mixture of pleasure and painat the base of amusement

• Focus on effect of humour• Release of nervous energy

Theories of humour

• Gives a necessary conditionFor humour – a ‘twist’. • Humour arises from showingsomething absurd fromsomething 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.)

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”.

Humour & AI

Humour theoryComputational

HumourJAPE-1 HAHAcronym

Humour Recognition

Applications of Computational

humour

ComputationalHumour

Definition

Areas

Our Focus

• Using computers in humourresearch.

• Modelling humour in a computationally tractable way.

• Humour Generation

• Humour Recognition

What is *computational* humour?

• Out of all forms, text-based / Verbal Humour• Humour in one-liners

Phonological

Morphological

Syntactic

• Same sounds, different meaning.

• Three ways:•Syllable substitutionE.g. What do short-sighted ghosts wear? Spooktacles. •Word substitutionE.g. How do u make gold soup? Put 14 carrots in it.•Metathesis (Reversal of sounds)

•Words with same surfacestructure.

E.g. : The book is read / red.

Computational Humour – Linguistic Ambiguity

• As a result of structure orsyntax of sentence.• Example: “Squad helps dogbite victims.”

A word is ambiguous if it has more than one meaning. (‘Ambiguous’ is thankfully not ambiguous. )

Humour & AI

Humour theoryComputational

HumourJAPE-1 HAHAcronym

Humour Recognition

Applications of Computational

humour

JAPE-1

JAPE-1

• Generates question-answer style puns using phonological similarities

•For example,What do you give an elephant that’s exhausted?Trunkquillizers.

Lexicon

Schemata

Template

•A set of lexemes.

•Lexeme is an abstract entity,roughly corresponding to ameaning or a phrase.

•In addition, a homonym base.

A set of relationships whichmust hold between the lexemes

JAPE-1 : Units

To produce the surface form of a joke from the lexemes

and relationships specifiedin an instantiated schema.

JAPE-1: Example

Lexicon

Schemata

Template

Lexeme : jumperSynonym : SweaterCategory: NounCountable: YesSpecifying adjective : Warm

“What will you get if you cross____ and ____?”Answer: _______

Humour & AI

Humour theoryComputational

HumourJAPE-1 HAHAcronym

Humour Recognition

Applications of Computational

humour

HAHAcronym

HAHAcronym

http://www.haha.itc.it

About HAHAcronym

Features

Examples

• European project• Humorous Agent for Humourous Acronyms.• Acronym Ironic Re-analyzer and generator

• 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 forConfusing machinery

Synset

WordNet

WordNet Domains

• group of data elements that are considered semantically equivalent for the purposes of information retrieval.• Eg. Person, Human, Individual

•A large 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.

HAHAcronym : Concepts

•Augment WordNet withdomain labels.

•Example, the word ‘bank’ has two labels – Economy and Geology.

HAHAcronym : Acronym modification

Acronym parsing and

construction of logical form

Recognizes individual constituents such as NP, VP, etc. using acronym grammar.

Choice of whatto 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.

HAHAcronym : Examples of Acr. Modification

CCTT

CHI •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.

Computer Human Interface.

Computer Harry_Truman Interface.

Unexpected result: due to rhyming of "human"

to "harry_truman"

HAHAcronym : Acronym generation

Input/Output

Example Input: Main concept +

Attribute

Output: A new funny acronym.

Concept: WritingAttribute: Creative

CAUSTIC – Creative Activity forUnconvincingly Sporadically

Talkative IndividualisticCommercials

Humour & AI

Humour theoryComputational

HumourJAPE-1 HAHAcronym

Humour Recognition

Applications of Computational

humour

HumourRecognition

Experiment

Strategies • Humorous data : One-liners• Non-humorous data :

• Sentences from BNC• Sentences from Reuters• Proverbs

• Stylistic features

• Content-based features

• Combination of the two

Humour Recognition

Stylistic Features

Content-based features• Antonymy( A clean desk is a sign ofcluttered drawer.)

• Alliteration( Infants don’t enjoy infancyas much as adults do adultery.)

• Naïve Bayes

• Uses joint probability of words and documents.

Humour Recognition : Strategies

Humour Recognition : Results

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.

Humour & AI

Humour theoryComputational

HumourJAPE-1 HAHAcronym

Humour Recognition

Applications of Computational

humour

Applications ofComputational

humour

• 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

Applications

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.

Applications : ALICE

Human: Will you marry me?

ALICE: Why don’t you just download me?

Applications : ALICE

• 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.

Conclusion

A conclusion is simply the place where you got tired of thinking.

The past was ‘ ’.

The future is ‘ ’.

We learnt…

computerization

humanization

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

Questions?Comments?

Suggestions?

Humour & AI

The past was computerization. The future is humanization.