title: A Computational Model of Linguistic Humor in Puns.
abstract: Humor plays an essential role in human interactions. Precisely what makes something funny, however, remains elusive. While research on natural language understanding has made significant advancements in recent years, there has been little direct integration of humor research with computational models of language understanding. In this paper, we propose two information-theoretic measures-ambiguity and distinctiveness-derived from a simple model of sentence processing. We test these measures on a set of puns and regular sentences and show that they correlate significantly with human judgments of funniness. Moreover, within a set of puns, the distinctiveness measure distinguishes exceptionally funny puns from mediocre ones. Our work is the first, to our knowledge, to integrate a computational model of general language understanding and humor theory to quantitatively predict humor at a fine-grained level. We present it as an example of a framework for applying models of language processing to understand higher level linguistic and cognitive phenomena.
authors.affiliation: Department of Psychology, Stanford University.
authors.forename: Justine T
authors.initials: JT
authors.lastname: Kao
authors.affiliation: Department of Linguistics, University of California at San Diego.
authors.forename: Roger
authors.initials: R
authors.lastname: Levy
authors.affiliation: Department of Psychology, Stanford University.
authors.forename: Noah D
authors.initials: ND
authors.lastname: Goodman
mesh_terms: D032882:Comprehension; D006801:Humans; D007802:Language; D008037:Linguistics; D014925:Wit and Humor as Topic
keywords: Ambiguity; Computational modeling; Humor; Language processing; Noisy channel