# AutoSense Model for Word Sense Induction

arXiv: https://arxiv.org/abs/1811.09242 · HTML: https://mlsanity.com/paper/1811.09242/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2018.

## Built on by (most cited first, 7 of 7)

- [Characterizing English Variation across Social Media Communities with BERT](https://mlsanity.com/paper/2102.06820/) (2021)
- [Large Scale Substitution-based Word Sense Induction](https://mlsanity.com/paper/2110.07681/) (2021)
- [PolyLM: Learning about Polysemy through Language Modeling](https://mlsanity.com/paper/2101.10448/) (2021)
- [In the LLM era, Word Sense Induction remains unsolved](https://mlsanity.com/paper/2603.11686/) (2026)
- [Multilingual Substitution-based Word Sense Induction](https://mlsanity.com/paper/2405.11086/) (2024)
- [Word Sense Induction with Hierarchical Clustering and Mutual Information Maximization](https://mlsanity.com/paper/2210.05422/) (2022)
- [From Ghazals to Sonnets: Decoding the Polysemous Expressions of Love Across Languages](https://mlsanity.com/paper/2510.15569/) (2025)

## Abstract (arXiv)

Word sense induction (WSI), or the task of automatically discovering multiple senses or meanings of a word, has three main challenges: domain adaptability, novel sense detection, and sense granularity flexibility. While current latent variable models are known to solve the first two challenges, they are not flexible to different word sense granularities, which differ very much among words, from aardvark with one sense, to play with over 50 senses. Current models either require hyperparameter tuning or nonparametric induction of the number of senses, which we find both to be ineffective. Thus, we aim to eliminate these requirements and solve the sense granularity problem by proposing AutoSense, a latent variable model based on two observations: (1) senses are represented as a distribution over topics, and (2) senses generate pairings between the target word and its neighboring word. These observations alleviate the problem by (a) throwing garbage senses and (b) additionally inducing fine-grained word senses. Results show great improvements over the state-of-the-art models on popular WSI datasets. We also show that AutoSense is able to learn the appropriate sense granularity of a word. Finally, we apply AutoSense to the unsupervised author name disambiguation task where the sense granularity problem is more evident and show that AutoSense is evidently better than competing models. We share our data and code here: https://github.com/rktamplayo/AutoSense.