Analysis and tuning of hierarchical topic models based on Renyi entropy approach.

ABSTRACT: Hierarchical topic modeling is a potentially powerful instrument for determining topical structures of text collections that additionally allows constructing a hierarchy representing the levels of topic abstractness. However, parameter optimization in hierarchical models, which includes finding an appropriate number of topics at each level of hierarchy, remains a challenging task. In this paper, we propose an approach based on Renyi entropy as a partial solution to the above problem. First, we introduce a Renyi entropy-based metric of quality for hierarchical models. Second, we propose a practical approach to obtaining the "correct" number of topics in hierarchical topic models and show how model hyperparameters should be tuned for that purpose. We test this approach on the datasets with t

SUBMITTER: Koltcov S 

PROVIDER: S-EPMC8330431 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature