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Poster
Humor in Word Embeddings: Cockamamie Gobbledegook for Nincompoops
Limor Gultchin · Genevieve Patterson · Nancy Baym · Nathaniel Swinger · Adam Kalai

Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #108
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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.

Keywords: Natural Language Processing · Other Applications · Recommender Systems · Representation Learning

Author Information

Limor Gultchin (University of Oxford)
Genevieve Patterson (TRASH)
Nancy Baym (Micr)
Nathaniel Swinger (Lexington High School)
Adam Kalai (Microsoft Research)

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