# Towards Resolving Word Ambiguity with Word Embeddings

arXiv: https://arxiv.org/abs/2307.13417 · HTML: https://mlsanity.com/paper/2307.13417/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 25 Jul 2023.

## Builds on (most cited first, 5 of 5)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [A Dual Embedding Space Model for Document Ranking](https://mlsanity.com/paper/1602.01137/) (2016)
- [To lemmatize or not to lemmatize: how word normalisation affects ELMo performance in word sense disambiguation](https://mlsanity.com/paper/1909.03135/) (2019)

## Built on by (most cited first, 1 of 1)

- [Fast Think-on-Graph: Wider, Deeper and Faster Reasoning of Large Language Model on Knowledge Graph](https://mlsanity.com/paper/2501.14300/) (2025)

## Abstract (arXiv)

Ambiguity is ubiquitous in natural language. Resolving ambiguous meanings is especially important in information retrieval tasks. While word embeddings carry semantic information, they fail to handle ambiguity well. Transformer models have been shown to handle word ambiguity for complex queries, but they cannot be used to identify ambiguous words, e.g. for a 1-word query. Furthermore, training these models is costly in terms of time, hardware resources, and training data, prohibiting their use in specialized environments with sensitive data. Word embeddings can be trained using moderate hardware resources. This paper shows that applying DBSCAN clustering to the latent space can identify ambiguous words and evaluate their level of ambiguity. An automatic DBSCAN parameter selection leads to high-quality clusters, which are semantically coherent and correspond well to the perceived meanings of a given word.