Computational Detection of Humor A Dream or a Nightmare? The Ontological Semantics Approach

This paper deals with computational detection of humor. It assumes that computational humor is a useful task for any number of reasons and in many applications. It discusses the computational linguistic/ semantic preconditions for computational humor and an ontological semantic approach to the task...

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Vydáno v:Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03 Ročník 3; s. 429 - 432
Hlavní autor: Taylor, Julia M.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: Washington, DC, USA IEEE Computer Society 15.09.2009
IEEE
Edice:ACM Conferences
Témata:
Application software
Computational intelligence
Computational linguistics
Computing methodologies > Artificial intelligence > Knowledge representation and reasoning
Computing methodologies > Artificial intelligence > Knowledge representation and reasoning > Semantic networks
Computing methodologies > Artificial intelligence > Natural language processing
Computing methodologies > Artificial intelligence > Natural language processing > Language resources
Computing methodologies > Machine learning > Machine learning approaches > Rule learning
Conferences
Humans
humor detection
Intelligent agent
Learning systems
natural language
Natural languages
Ontologies
semantics
USA Councils
semantics
humor detection
natural language
ontologies
ISBN:0769538010, 9780769538013
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Shrnutí:This paper deals with computational detection of humor. It assumes that computational humor is a useful task for any number of reasons and in many applications. It discusses the computational linguistic/ semantic preconditions for computational humor and an ontological semantic approach to the task of humor detection, based on direct and comprehensive access to meaning rather than on trying to guess it through statistical-cum-syntactical keyword methods. The paper is informed by the experience of designing and implementing a humor detection model, whose decent success rate confirmed some of the assumptions while its misses made other ideas prominent, including the necessity of full text comprehension. The bulk of the paper explains how the comprehensive representation of meaning and, most importantly how unstructured natural language text is automatically translated into the ontologically defined text meaning representations that can be used then to detect humor in them, if any, automatically.
ISBN:0769538010
9780769538013
DOI:10.1109/WI-IAT.2009.318