The Construction of a Pun Generator for Language Skills Development Humor Generation SoSe 2010 Lourdes Lara Tapia.

Slides:



Advertisements
Similar presentations
Design, prototyping and construction
Advertisements

Communication Strategies and Technology Solutions for Students with ASD Lyn Phoenix Assistive Technology Coordinator S.T.A.R.S. Program Amy Percassi,
Teacher Talk The Importance of a Language Rich Preschool Environment Preschool Coordinators meeting October 18, 2005 Patsy L.Pierce, Ph.D., Office of School.
“How can I learn AI?” Lindsay Evett, Alan Battersby, David Brown, SCI NTU Penny Standen, DRA UN.
Introduction to Computational Linguistics
Introduction to Computational Linguistics
SEMANTICALLY RICH EDUCATIONAL WORD GAMES ENHANCED BY SOFTWARE AGENTS Boyan Bontchev, Sergey Varbanov, Dessislava Vassileva INFOS 2011 Rzeszów - Polańczyk,
User Interfaces 4 BTECH: IT WIKI PAGE:
Language and Cognition Colombo, June 2011 Day 8 Aphasia: disorders of comprehension.
Synthetic Speech: Does it increase social interaction? Melissa Bairos, Emily Emanuel, Aviva Krauthammer, Jen Perkins, Holly Reis, and Beth Zaglin.
Electrical Engineering Department, The Hong Kong Polytechnic University Technical Presentations.
Disclaimer : The jokes during the seminar were generated either by AI (Artificial Intelligence) or by AI (Aaditya’s Intelligence). The bottomline, AI is.
INTERNATIONAL CONFERENCE ON NATURAL LANGUAGE PROCESSING NLP-AI IIIT-Hyderabad CIIL, Mysore ICON DECEMBER, 2003.
Applied Computing, University of Dundee Annalu Waller Dave O’Mara
Introduction to Computational Linguistics Lecture 2.
Case Presentation Case #4, Esther Jessica Cassellius April LaCoursiere Meghan Neu.
BY CHRIS ANDERSON Creating a MIDI Generator Program.
Informative Speaking.
Second Language Acquisition and Real World Applications Alessandro Benati (Director of CAROLE, University of Greenwich, UK) Making.
Fundamentals: Linguistic principles
Teaching Writing to Young Learner. The Young Language Learner According to Cameron (2001) level of young learners are: Age 3-6 years old: very young learner.
Emotional Intelligence and Agents – Survey and Possible Applications Mirjana Ivanovic, Milos Radovanovic, Zoran Budimac, Dejan Mitrovic, Vladimir Kurbalija,
The Computer Science Course at Omar Al-Mukhtar University, Libya The Computer Science Course at Omar Al-Mukhtar University, Libya User-Centered Design.
Mapping Fundamental Business Process Modelling Language to the Web Services Ontology Gayathri Nadarajan and Yun-Heh Chen-Burger Centre for Intelligent.
CSD 5400 REHABILITATION PROCEDURES FOR THE HARD OF HEARING Language and Speech of Deaf and Hard-of-Hearing Characteristics and Concerns Language Acquisition.
Researching for a Debate
March 1, 2009 Dr. Muhammed Al-Mulhem 1 ICS 482 Natural Language Processing INTRODUCTION Muhammed Al-Mulhem March 1, 2009.
Artificial Intelligence. Agenda StartEnd Introduction AI Future Recent Developments Turing Test Turing Test Evaluation.
COMPUTATIONAL HUMOUR Seminar Presentation Rohan, Avijit, Praveen, Ashutosh, Hemendra.
Lecture 1, 7/21/2005Natural Language Processing1 CS60057 Speech &Natural Language Processing Autumn 2005 Lecture 1 21 July 2005.
CAREERS IN LINGUISTICS OUTSIDE OF ACADEMIA CAREERS IN INDUSTRY.
Communicative Language Teaching Vocabulary
Lecture 12: 22/6/1435 Natural language processing Lecturer/ Kawther Abas 363CS – Artificial Intelligence.
A single case series of narrative interaction between children who use speech generating devices and their educational staff Pippa Bailey*, Karen Bunning,
Building a Lexical Database for an Interactive Joke-Generator Ruli Manurung, Dave O’Mara, Helen Pain, Graeme Ritchie, Annalu Waller, Rolf Black We are.
COMPUTER ASSISTED / AIDED LANGUAGE LEARNING (CALL) By: Sugeili Liliana Chan Santos.
The ID process Identifying needs and establishing requirements Developing alternative designs that meet those requirements Building interactive versions.
1 Computational Linguistics Ling 200 Spring 2006.
Artificial Intelligence Introductory Lecture Jennifer J. Burg Department of Mathematics and Computer Science.
Welcome to Unit 5 Seminar: Stages of Languge Acquisition Learning The Language.
Introduction to CL & NLP CMSC April 1, 2003.
Presentations A General Introduction into the basic principles.
Accessibility IS 101Y/CMSC 101Y November 19, 2013 Carolyn Seaman University of Maryland Baltimore County.
Mining Topic-Specific Concepts and Definitions on the Web Bing Liu, etc KDD03 CS591CXZ CS591CXZ Web mining: Lexical relationship mining.
Yarmouk University Department of Computer Information Systems CIS 499 Yarmouk University Department of Computer Information Systems CIS 499 Yarmouk University.
October 2005CSA3180 NLP1 CSA3180 Natural Language Processing Introduction and Course Overview.
CSA2050 Introduction to Computational Linguistics Lecture 1 Overview.
School of something FACULTY OF OTHER Facing Complexity Using AAC in Human User Interface Design Lisa-Dionne Morris School of Mechanical Engineering
I.T. supporting older and disabled people: Prof. Alan Newell, MBE, FRSE, Applied Computing, University of Dundee, Scotland, UK.
Digital Libraries1 David Rashty. Digital Libraries2 “A library is an arsenal of liberty” Anonymous.
EVALUATION PROfessional network of Master’s degrees in Informatics as a Second Competence – PROMIS ( TEMPUS FR-TEMPUS-JPCR)
COMPUTATIONAL HUMOUR Siddhartha G Naga Varun Sachin R.
Introduction & Overview Informative vs. Persuasive Speeches Types of informative speaking Techniques of informative speaking Organization and Structure.
HCI Meeting 1 Thursday, August 26. Class Activities [1] Student questionnaire Answer the following questions: 1.When and where was the computer mouse.
Survey on Long Queries in Keyword Search : Phrase-based IR Sungchan Park
1 FollowMyLink Individual APT Presentation First Talk February 2006.
Speech Processing 1 Introduction Waldemar Skoberla phone: fax: WWW:
King Faisal University جامعة الملك فيصل Deanship of E-Learning and Distance Education عمادة التعلم الإلكتروني والتعليم عن بعد [ ] 1 جامعة الملك فيصل عمادة.
Modules(Units) Course contents: This book as you checked has 3 modules We finished Module 1 -which (has 7 units) As in the following Box.
Computer Systems Architecture Edited by Original lecture by Ian Sunley Areas: Computer users Basic topics What is a computer?
Usage-Based Phonology Anna Nordenskjöld Bergman. Usage-Based Phonology overall approach What is the overall approach taken by this theory? summarize How.
AUTHOR PRADEEP KUMAR B.tech 1 st year CSE branch Gnyana saraswati college of eng. & technology Dharmaram(b)
Create a blog Skills: create, modify and post to a blog
PSYC 206 Lifespan Development Bilge Yagmurlu.
CHAPTER 1 Introduction BIC 3337 EXPERT SYSTEM.
Chapter 6. Data Collection in a Wizard-of-Oz Experiment in Reinforcement Learning for Adaptive Dialogue Systems by: Rieser & Lemon. Course: Autonomous.
Word Play Generation Vivaek Shivakumar April 7, 2010.
MUMT611: Music Information Acquisition, Preservation, and Retrieval
Table of Contents – Part B
Presentation transcript:

The Construction of a Pun Generator for Language Skills Development Humor Generation SoSe 2010 Lourdes Lara Tapia

Humor Generation2 June 29, 2010 Overview  Introduction.  Early pun generators.  JAPE.  STANDUP.  STANDUP in the Praxis.  Evaluation  Conclusion.  References.

Humor Generation3 June 29, 2010 Introduction  What is a Pun Generator?  A pun Generator is a Computer Program which generates jokes.  What is a Joke?  It is a short text which provoke laughter.  A joke has normally a Punchline.  There are different kind of Jokes:  Punning riddles

Humor Generation4 June 29, 2010 Introduction  A punning riddle is a simple question-answer joke in which the answer makes a play on words:  What do you call a good bye that has a tooth?  A saw long.

Humor Generation5 June 29, 2010 Introduction  What kind of ambiguity is used here?  What do you call a good bye that has a tooth?  A saw long. SynonymMeronym Homophone  A So long Phonetic similarity Semantic relation

Humor Generation6 June 29, 2010 Early pun generators  Raskin (1985):  Incongruity Theory.  Lesard & Levison (1992):  VINCI: Tom Swift  “we must hurry”, said Tom Swiftly.  “I hate Math”, Tom added  Binsted & Ritchie (1994):  JAPE:  Punning riddle uses phonological and semantical ambiguity  Used a large lexicon (WordNet)  Properly controlled evaluation of the output was carried out.

Humor Generation7 June 29, 2010 Early pun generators  Venour (1999):  The Homonym Common Phrase Pun (HCPP).  A one-sentence set-up &  A punning punchline.  Mechanismus are similar to those used in JAPE  McKay (2002):  WISCRAIC:  Simple puns in 3-different linguistic forms:  Question-answer, single and two-sentences sequence.  Support 2nd-language learning

Humor Generation8 June 29, 2010 Early pun generators  Nijholt (2003):  Communication with machines.  Stock et al. (2005):  HAHAcronym:  Acronym  funny concepts  Concept  funny Acronym  Mihalcea & Strapparava (2006):  Techniques to humor recognition:  Humurous and non-humorous.

Humor Generation9 June 29, 2010 JAPE  Joke Analysis and Production Engine.  What is JAPE?  Computer Program  In Prolog by Binsted in  Several Version  JAPE-1 (pilot version) & JAPE2  JAPE-3 & JAPE-4 (more flexible dictionary module)   STANDUP in 2008.

Humor Generation10 June 29, 2010 JAPE  JAPE produced short texts  punning riddles:  What is the difference between a pretty glove and a silent cat?  One is a cute mitten, the other is a mute kitten.  The Jokes were reliably distinguished from Non- Jokes.  The best of these were published in joke books for children.

Humor Generation11 June 29, 2010 JAPE  The three main strategies used to create phonological ambiguity:  syllable substitution,  word substitution &  Metathesis  Joke-construction mechanisms.  Very similar to those in STANDUP

Humor Generation12 June 29, 2010 JAPE Fig. by Ritchie in “The JAPE riddle generator: technical specification”, p. 4

Humor Generation13 June 29, 2010 JAPE  Deficiencies:  Few parameters available for variation.  There was no way to guide the software.  No real user interface.  The search for suitable words could be slow, unintelligent and exhaustive.  Good intelligible jokes was very small.  No facilities to compare words for phonological or semantically ambiguity.

Humor Generation14 June 29, 2010 STANDUP  System To Augment Non-speakers Dialogue Using Puns.  This Program is aimed at young children, and lets them play with words and phrases by building punning riddles through a simple interactive user-interface.  Allow young children to explore the language.  Children with Complex Communication Needs (CNN).  Punning riddle.  “Language playground”

Humor Generation15 June 29, 2010 STANDUP Schema Header Lexical Precondition Question Spec.Answer Spec. Keywords Description Rules Header Preconditions Template Spec. Templates Phrasal Question or Answer Header Body

Humor Generation16 June 29, 2010 Fig.

Humor Generation17 June 29, 2010 STANDUP  What do you call a shout with a window?  A computer scream.

Humor Generation18 June 29, 2010 STANDUP 11 Schema (kind of joke) Header: Newelan2(NP,A,B,HomB) Lexical Precondition: Nouncomp(NP,A,B), Homoph(B,HomB), Noun(HomB) Question Spec.: {Shareprop (NP,HomB)} Answer Spec.: {phrase (A,HomB)} Keywords: [NP,HomB] Description Rules Header: Shareprop {X,Y} Preconditions: Meronym(X,MerX), Syn(Y,SynY) Template Spec.: [merHyp, MerX, SynY] Templates Phrasal (finish touches) Question (What is the diff…?) Answer (They’re both…) Header Body

Humor Generation19 June 29, 2010 STANDUP 11 Schema (kind of joke) Header: Newelan2(NP:computer screen, A: computer, B: screen, HomB: scream) Lexical Precondition: Nouncomp(NP,A,B), Homoph(B,HomB), Noun(HomB) Question Spec.: {Shareprop (computer screen, scream)} Answer Spec.: {phrase (computer, scream)} Keywords: [NP,HomB] Description Rules Header: Shareprop {computer screen, scream} Preconditions: Meronym(computer screen, window), Syn(scream, shout) Template Spec.: [merSyn, window, shout] Templates Phrasal (finish touches) Question (What is the diff…?) Answer (They’re both…) Header [merSyn, window, shout] Body What do you call a NP(X,Y) Body NP(shout) with a NP(window) Question What do you call a shout with a window? Header A shout with a window

Humor Generation20 June 29, 2010 STANDUP 11 Schema (kind of joke) Header: Newelan2(NP:computer screen, A: computer, B: screen, HomB: scream) Lexical Precondition: Nouncomp(NP,A,B), Homoph(B,HomB), Noun(HomB) Question Spec.: {Shareprop (computer screen, scream)} Answer Spec.: {phrase (computer, scream)} Keywords: [NP,HomB] Description Rules Header: Shareprop {computer, scream} Preconditions Template Spec.: [simple, computer, scream] Templates Phrasal Question or Answer Header Body Phrasal: A computer scream

Humor Generation21 June 29, 2010 STANDUP-Lexicon  WordNet as JAPE +  Phonetic similarity.  Speech Output.  Picture Support.  Topics.  Familiarity of words.  Vocabulary restriction.

Humor Generation22 June 29, 2010 STANDUP-Facilities  Joke telling:  VOCA: Voice-Output Communication Aid.  assists people who are unable to use natural speech to express their needs and exchange information with other people during a conversation.  User Profiles:  Username.  Two kind of data:  Option settings.  Personal Data.  Standard Package:  Beginner  Touchscreen-user.

Humor Generation23 June 29, 2010 STANDUP-Facilities  Logging:  Logged in a Disc file:  Allows researchers to study usage as required.  Log player  Dump the simulated re-runs into a video file.

Humor Generation24 June 29, 2010 STANDUP-Software ..\STANDUP Simple.bat..\STANDUP Simple.bat

Humor Generation25 June 29, 2010 STANDUP-Evaluation  Evaluate the effectiveness of the software.  No ambitious but qualitative study.  A group of 9 children (8-12years old) with cerebral palsy.  Scholars used the software spontaneously,  Found the “Tell the jokes-function” amazing and  Re-told the jokes afterwards.  8 children reacted very positively  1 of the older boys complained about the quality of the Jokes.  Anecdotal evidence: Children’s communication had improved.

Humor Generation26 June 29, 2010 STANDUP-Evaluation  In the post-testing:  The Preschool and Primary Inventory of Phonological Awareness, PIPA, showed no sign of improved.  Clinical Evaluation of Language Fundamentals, CELF, only the older boy, who complained, showed no sign of improved.

Humor Generation27 June 29, 2010 Conclusion  Humor is one of the most sophisticated forms of human intelligence.  On the cognitive side humor has two very important properties:  it helps getting and keeping people’s attention.  it helps remembering.  On the artificial intelligence side computational humor is a challenge with implications for many classical fields.

Humor Generation28 June 29, 2010 Conclusion  The development of all its facets is not something for the near future, the phenomena are too complex.  Simple puns, at least, can be modelled formally, and can be generated by a program.  The software is definitely usable for a practical application by children with communication disabilities to develop their linguistic skills.

Humor Generation29 June 29, 2010 Discussion  Questions   Opinion or   Comments

Humor Generation30 June 29, 2010 Thank you for your attention

Humor Generation31 June 29, 2010 References  Binsted, K Machine humour: An implemented model of puns. Ph. D. thesis, University of Edinburgh, Edinburgh, Scotland.  Binsted, K., H. Pain, and G. Ritchie Children's evaluation of computer generated punning riddles. Pragmatics and Cognition 5 (2),  Manurung, R., G. Ritchie, H. Pain, A. Waller, D. O'Mara, R. Black (2008). The construction of a pun generator for language skills development. Applied Artificial Intelligence, 22(9) pp  Ritchie, G Current directions in computational humour. Artificial Intelligence Review 16 (2),  Ritchie, G The JAPE riddle generator: technical specification. Informatics Research Report EDI-INF-RR-0158, School of Informatics, University of Edinburgh, Edinburgh.  Stock, O. and C. Strapparava HAHAcronym: Humorous agents for humorous acronyms. Humor: International Journal of Humor Research 16 (3),   