# Topological Data Analysis for Word Sense Disambiguation

arXiv: https://arxiv.org/abs/2203.00565 · HTML: https://mlsanity.com/paper/2203.00565/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 1 Mar 2022.

## Builds on (most cited first, 3 of 3)

- [Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology Based Representations](https://mlsanity.com/paper/1705.10900/) (2017)
- [A Brief History of Persistence](https://mlsanity.com/paper/1809.03624/) (2018)
- [Local Homology of Word Embeddings](https://mlsanity.com/paper/1810.10136/) (2018)

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

- [Topological Data Analysis Applications in Natural Language Processing: A Survey](https://mlsanity.com/paper/2411.10298/) (2024)

## Abstract (arXiv)

We develop and test a novel unsupervised algorithm for word sense induction and disambiguation which uses topological data analysis. Typical approaches to the problem involve clustering, based on simple low level features of distance in word embeddings. Our approach relies on advanced mathematical concepts in the field of topology which provides a richer conceptualization of clusters for the word sense induction tasks. We use a persistent homology barcode algorithm on the SemCor dataset and demonstrate that our approach gives low relative error on word sense induction. This shows the promise of topological algorithms for natural language processing and we advocate for future work in this promising area.