# An Unsupervised Word Sense Disambiguation System for Under-Resourced Languages

arXiv: https://arxiv.org/abs/1804.10686 · HTML: https://mlsanity.com/paper/1804.10686/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2018.

## Built on by (most cited first, 2 of 2)

- [Chinese Word Sense Embedding with SememeWSD and Synonym Set](https://mlsanity.com/paper/2206.14388/) (2022)
- [Context-Aware Semantic Similarity Measurement for Unsupervised Word Sense Disambiguation](https://mlsanity.com/paper/2305.03520/) (2023)

## Abstract (arXiv)

In this paper, we present Watasense, an unsupervised system for word sense disambiguation. Given a sentence, the system chooses the most relevant sense of each input word with respect to the semantic similarity between the given sentence and the synset constituting the sense of the target word. Watasense has two modes of operation. The sparse mode uses the traditional vector space model to estimate the most similar word sense corresponding to its context. The dense mode, instead, uses synset embeddings to cope with the sparsity problem. We describe the architecture of the present system and also conduct its evaluation on three different lexical semantic resources for Russian. We found that the dense mode substantially outperforms the sparse one on all datasets according to the adjusted Rand index.