Topic Modeling With Topological Data Analysis
Ciarán Byrne, Danijela Horak, Karo Moilanen, Amandla Mabona · 2022
Recent unsupervised topic modelling approaches that use clustering techniques on word, token or document embeddings can extract coherent topics.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.