Paper Info
Title
Hierarchical Bayesian text modeling for the unsupervised joint analysis of latent topics and semantic clusters
Abstract
Topic modeling can be unified synergically with document clustering. In this manuscript, we propose two innovative unsupervised approaches for the combined modeling and interrelated accomplishment of the two tasks. Both approaches rely on respective Bayesian generative models of topics, contents and clusters in textual corpora. Such models treat topics and clusters as linked latent factors in document wording. In particular, under the generative model of the second approach, textual documents are characterized by topic distributions, that are allowed to vary around the topic distributions of their membership clusters. Within the devised models, algorithms are designed to implement Rao-Blackwellized Gibbs sampling together with parameter estimation. These are derived mathematically for carrying out topic modeling with document clustering in a simultaneous and interrelated manner.
Year
DOI
Venue
2022
10.1016/j.ijar.2022.05.002
International Journal of Approximate Reasoning
Keywords
DocType
Volume
Bayesian text analysis,Topic modeling,Document clustering,Hierarchical priors
Journal
147
ISSN
Citations 
PageRank 
0888-613X
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Gianni Costa123524.04
Riccardo Ortale228227.46