Effective interrelation of Bayesian nonparametric document clustering and embedded-topic modeling

Topic modeling can be synergically interrelated with document clustering. We present an innovative unsupervised approach to the interrelationship of topic modeling with document clustering. The devised approach exploits Bayesian generative modeling and posterior inference, to seamlessly unify and jointly carry out the two tasks, respectively. Specifically, a Bayesian nonparametric model of text collections, formulates an unprecedented interrelationship of word-embedding topics with a Dirichlet process mixture of cluster components. The latter enables countably infinite clusters and permits the automatic inference of their actual number in a statistically principled manner. All latent clusters and topics under the foresaid model are inferred through collapsed Gibbs sampling and parameter estimation. An extensive empirical study of the presented approach is effected on benchmark real-world corpora of text documents. The experimental results demonstrate its higher effectiveness in partitioning text collections and coherently discovering their semantics, compared to state-of-the-art competitors and tailored baselines. Computational efficiency is also looked into under different conditions, in order to provide an insightful analysis of scalability. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

An innovative unsupervised approach is presented for the interrelationship of topic modeling with document clustering, utilizing Bayesian generative modeling and posterior inference. The approach seamlessly unifies and jointly carries out the two tasks, enabling automatic inference of the relationships between word-embedding topics and cluster components. Extensive empirical study on benchmark real-world corpora demonstrates the method's higher effectiveness in partitioning text collections and discovering their semantics.