# Exploring Dowker Homology for Sentence Similarity

arXiv: https://arxiv.org/abs/2608.22909 · HTML: https://mlsanity.com/paper/2608.22909/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 13 references that are in the map.
- First posted on arXiv 24 Aug 2026.

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

- [SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation](https://mlsanity.com/paper/1708.00055/) (2017)
- [Persistence Images: A Stable Vector Representation of Persistent Homology](https://mlsanity.com/paper/1507.06217/) (2015)
- [Representation Topology Divergence: A Method for Comparing Neural Network Representations](https://mlsanity.com/paper/2201.00058/) (2021)
- [Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology Based Representations](https://mlsanity.com/paper/1705.10900/) (2017)
- [Fixation Sequences as Time Series: A Topological Approach to Dyslexia Detection](https://mlsanity.com/paper/2604.21698/) (2026)

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

- [Embedding Models Measure in Peculiar Ways](https://mlsanity.com/paper/2609.20821/) (2026)

## Abstract (arXiv)

Dowker homology is a topological tool that may be used to analyze the relative position of two point clouds living in a common space. We investigate whether Dowker homology captures sentence similarity information by treating the embeddings of the tokens that constitute a sentence pair as a pair of point clouds in the latent space of a transformer model, using both models that have and have not been fine-tuned for sentence similarity. We find that Dowker homology captures sentence similarity information, as measured by regressing Dowker homology features onto ground-truth similarity scores, and that it can be used for visual inspection of similarity data and models. In an attempt to make Dowker homology readily applicable, we derive from it single-number summaries that we expect to capture sentence similarity directly. These turn out to work reasonably well, but without outperforming standard sentence similarity measures based on established pooling methods.