2019/02/08 by Limor Gultchin, Gultchin, Limor, Geneviève Patterson +9
Computer Science · Mathematics · Psychology · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Humor Studies and Applications #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Natural Language Processing Techniques #Topic Modeling #cs.CL #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1902.02783
openalex publication_date 2019/02/08 · arxiv created 2019/05/24 · arxiv updated 2019/05/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.