Skip to yearly menu bar Skip to main content


Humor in Word Embeddings: Cockamamie Gobbledegook for Nincompoops

Limor Gultchin · Genevieve Patterson · Nancy Baym · Nathaniel Swinger · Adam Kalai

Pacific Ballroom #108

Keywords: [ Representation Learning ] [ Recommender Systems ] [ Other Applications ] [ Natural Language Processing ]


While humor is often thought to be beyond the reach of Natural Language Processing, we show that several aspects of single-word humor correlate with simple linear directions in Word Embeddings. In particular: (a) the word vectors capture multiple aspects discussed in humor theories from various disciplines; (b) each individual's sense of humor can be represented by a vector, which can predict differences in people's senses of humor on new, unrated, words; and (c) upon clustering humor ratings of multiple demographic groups, different humor preferences emerge across the different groups. Humor ratings are taken from the work of Engelthaler and Hills (2017) as well as from an original crowdsourcing study of 120,000 words. Our dataset further includes annotations for the theoretically-motivated humor features we identify.

Live content is unavailable. Log in and register to view live content