Making Computers Laugh: Investigations in Automatic Humor Recognition

Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pages 531–538, Vancouver, October 2005. c⃝2005 Association for Computational Linguistics Making Computers Laugh: Investigations in Automatic Humor Recognition Rada Mihalcea Department of Computer Science University of North Texas Denton, TX, 76203, USA rada@cs.unt.edu Carlo Strapparava Istituto per la Ricerca Scientifica e Tecnologica ITC – irst I-38050, Povo, Trento, Italy strappa@itc.it Abstract Humor is one of the most interesting and puzzling aspects of human behavior. De- spite the attention it has received in fields such as philosophy, linguistics, and psy- chology, there have been only few at- tempts to create computational models for humor recognition or generation. In this paper, we bring empirical evidence that computational approaches can be success- fully applied to the task of humor recogni- tion. Through experiments performed on very large data sets, we show that auto- matic classification techniques can be ef- fectively used to distinguish between hu- morous and non-humorous texts, with sig- nificant improvements observed over apri- ori known baselines. 1 Introduction ... pleasure has probably been the main goal all along. But I hesitate to admit it, because computer scientists want to maintain their image as hard-working individuals who deserve high salaries. Sooner or later society will realize that certain kinds of hard work are in fact admirable even though they are more fun than just about anything else. (Knuth, 1993) Humor is an essential element in personal com- munication. While it is merely considered a way to induce amusement, humor also has a positive ef- fect on the mental state of those using it and has the ability to improve their activity. Therefore computa- tional humor deserves particular attention, as it has the potential of changing computers into a creative and motivational tool for human activity (Stock et al., 2002; Nijholt et al., 2003). Previous work in computational humor has fo- cused mainly on the task of humor generation (Stock and Strapparava, 2003; Binsted and Ritchie, 1997), and very few attempts have been made to develop systems for automatic humor recognition (Taylor and Mazlack, 2004). This is not surprising, since, from a computational perspective, humor recogni- tion appears to be significantly more subtle and dif- ficult than humor generation. In this paper, we explore the applicability of computational approaches to the recognition of ver- bally expressed humor. In particular, we investigate whether automatic classification techniques are a vi- able approach to distinguish between humorous and non-humorous text, and we bring empirical evidence in support of this hypothesis through experiments performed on very large data sets. Since a deep comprehension of humor in all of its aspects is probably too ambitious and beyond the existing computational capabilities, we chose to restrict our investigation to the type of humor found in one-liners. A one-liner is a short sen- tence with comic effects and an interesting linguistic structure: simple syntax, deliberate use of rhetoric devices (e.g. alliteration, rhyme), and frequent use of creative language constructions meant to attract the …

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