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LEARNING TO LAUGH (AUTOMATICALLY): COMPUTATIONAL MODELS FOR HUMOR RECOGNITION
Author(s) -
Mihalcea Rada,
Strapparava Carlo
Publication year - 2006
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/j.1467-8640.2006.00278.x
Subject(s) - humor research , computer science , a priori and a posteriori , task (project management) , artificial intelligence , computational linguistics , natural language processing , computational model , speech recognition , pattern recognition (psychology) , machine learning , psychology , epistemology , social psychology , philosophy , management , economics
Humor is one of the most interesting and puzzling aspects of human behavior. Despite the attention it has received in fields such as philosophy, linguistics, and psychology, there have been only few attempts to create computational models for humor recognition or generation. In this article, we bring empirical evidence that computational approaches can be successfully applied to the task of humor recognition. Through experiments performed on very large data sets, we show that automatic classification techniques can be effectively used to distinguish between humorous and non‐humorous texts, with significant improvements observed over a priori known baselines.