# Don't Neglect the Obvious: On the Role of Unambiguous Words in Word Sense Disambiguation

arXiv: https://arxiv.org/abs/2004.14325 · HTML: https://mlsanity.com/paper/2004.14325/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2020.

## Built on by (most cited first, 4 of 4)

- [Let's Play Mono-Poly: BERT Can Reveal Words' Polysemy Level and Partitionability into Senses](https://mlsanity.com/paper/2104.14694/) (2021)
- [LMMS Reloaded: Transformer-based Sense Embeddings for Disambiguation and Beyond](https://mlsanity.com/paper/2105.12449/) (2021)
- [FEWS: Large-Scale, Low-Shot Word Sense Disambiguation with the Dictionary](https://mlsanity.com/paper/2102.07983/) (2021)
- [One Sense per Translation](https://mlsanity.com/paper/2106.06082/) (2021)

## Abstract (arXiv)

State-of-the-art methods for Word Sense Disambiguation (WSD) combine two different features: the power of pre-trained language models and a propagation method to extend the coverage of such models. This propagation is needed as current sense-annotated corpora lack coverage of many instances in the underlying sense inventory (usually WordNet). At the same time, unambiguous words make for a large portion of all words in WordNet, while being poorly covered in existing sense-annotated corpora. In this paper, we propose a simple method to provide annotations for most unambiguous words in a large corpus. We introduce the UWA (Unambiguous Word Annotations) dataset and show how a state-of-the-art propagation-based model can use it to extend the coverage and quality of its word sense embeddings by a significant margin, improving on its original results on WSD.