The Funny Thing About Incongruity: A Computational Model of Humor in Puns

Abstract

What makes something funny? Humor theorists posit that incongruity---perceiving a situation from different viewpoints and finding the resulting interpretations to be incompatible---contributes to sensations of mirth. In this paper, we use a computational model of sentence comprehension to formalize incongruity and test its relationship to humor in puns. By combining a noisy channel model of language comprehension and standard information theoretic measures, we derive two dimensions of incongruity---ambiguity of meaning and distinctiveness of viewpoints---and use them to predict humans' judgments of funniness. Results showed that both ambiguity and distinctiveness are significant predictors of humor. Additionally, our model automatically identifies specific features of a pun that make it amusing. We thus show how a probabilistic model of sentence comprehension can help explain essential features of the complex phenomenon of linguistic humor.


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