Topic Modeling with Topological Data Analysis

Abstract

Recent unsupervised topic modelling approaches that use clustering techniques on word, token or document embeddings can extract coherent topics. However, a common limitation of such approaches is that they reveal nothing about inter-topic relationships which are essential in many real-world application domains. We present an unsupervised topic modelling method which harnesses Topological Data Analysis (TDA) to extract a topological skeleton of the manifold upon which contextualised word embeddings lie. We demonstrate that our approach, which performs on par with a recent baseline, is able to construct a network of coherent topics together with meaningful relationships between them.

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