# Word Sense Disambiguation for 158 Languages using Word Embeddings Only

arXiv: https://arxiv.org/abs/2003.06651 · HTML: https://mlsanity.com/paper/2003.06651/

## Key facts

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

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

- [ArabGlossBERT: Fine-Tuning BERT on Context-Gloss Pairs for WSD](https://mlsanity.com/paper/2205.09685/) (2022)
- [Sentiment analysis and opinion mining on educational data: A survey](https://mlsanity.com/paper/2302.04359/) (2023)
- [RuDSI: graph-based word sense induction dataset for Russian](https://mlsanity.com/paper/2209.13750/) (2022)
- [Low-Resource Machine Translation through the Lens of Personalized Federated Learning](https://mlsanity.com/paper/2406.12564/) (2024)

## Abstract (arXiv)

Disambiguation of word senses in context is easy for humans, but is a major challenge for automatic approaches. Sophisticated supervised and knowledge-based models were developed to solve this task. However, (i) the inherent Zipfian distribution of supervised training instances for a given word and/or (ii) the quality of linguistic knowledge representations motivate the development of completely unsupervised and knowledge-free approaches to word sense disambiguation (WSD). They are particularly useful for under-resourced languages which do not have any resources for building either supervised and/or knowledge-based models. In this paper, we present a method that takes as input a standard pre-trained word embedding model and induces a fully-fledged word sense inventory, which can be used for disambiguation in context. We use this method to induce a collection of sense inventories for 158 languages on the basis of the original pre-trained fastText word embeddings by Grave et al. (2018), enabling WSD in these languages. Models and system are available online.