# Humor in Word Embeddings: Cockamamie Gobbledegook for Nincompoops

arXiv: https://arxiv.org/abs/1902.02783 · HTML: https://mlsanity.com/paper/1902.02783/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Feb 2019.

## Built on by (most cited first, 3 of 3)

- [PEFT-U: Parameter-Efficient Fine-Tuning for User Personalization](https://mlsanity.com/paper/2407.18078/) (2024)
- [How Did This Get Funded?! Automatically Identifying Quirky Scientific Achievements](https://mlsanity.com/paper/2106.03048/) (2021)
- [Witscript: A System for Generating Improvised Jokes in a Conversation](https://mlsanity.com/paper/2302.02008/) (2023)

## Abstract (arXiv)

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.