Local Homology of Word Embeddings

2018/10/24 by Tadas Temčinas, Temčinas, Tadas · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · #Algebraic Topology (math.AT) #Bioinformatics and Genomic Networks #Computation and Language (cs.CL) #FOS: Computer and information sciences #FOS: Mathematics #Topological and Geometric Data Analysis

paper · pdf · doi:10.48550/arxiv.1810.10136

openalex publication_date 2018/10/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Topological data analysis (TDA) has been widely used to make progress on a number of problems. However, it seems that TDA application in natural language processing (NLP) is at its infancy. In this paper we try to bridge the gap by arguing why TDA tools are a natural choice when it comes to analysing word embedding data. We describe a parallelisable unsupervised learning algorithm based on local homology of datapoints and show some experimental results on word embedding data. We see that local homology of datapoints in word embedding data contains some information that can potentially be used to solve the word sense disambiguation problem.

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