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Analyzing the influence of hyper-parameters and regularizers of topic modeling in terms of Renyi entropy

Analyzing the influence of hyper-parameters and regularizers of topic modeling in terms of Renyi entropy
Analyzing the influence of hyper-parameters and regularizers of topic modeling in terms of Renyi entropy
Topic modeling is a popular technique for clustering large collections of text documents. A variety of different types of regularization is implemented in topic modeling. In this paper, we propose a novel approach for analyzing the influence of different regularization types on results of topic modeling. Based on Renyi entropy, this approach is inspired by the concepts from statistical physics, where an inferred topical structure of a collection can be considered an information statistical system residing in a non-equilibrium state. By testing our approach on four models—Probabilistic Latent Semantic Analysis (pLSA), Additive Regularization of Topic Models (BigARTM), Latent Dirichlet Allocation (LDA) with Gibbs sampling, LDA with variational inference (VLDA)—we, first of all, show that the minimum of Renyi entropy coincides with the “true” number of topics, as determined in two labelled collections. Simultaneously, we find that Hierarchical Dirichlet Process (HDP) model as a well-known approach for topic number optimization fails to detect such optimum. Next, we demonstrate that large values of the regularization coefficient in BigARTM significantly shift the minimum of entropy from the topic number optimum, which effect is not observed for hyper-parameters in LDA with Gibbs sampling. We conclude that regularization may introduce unpredictable distortions into topic models that need further research.
394
Koltsov, Sergei
022812a6-4063-4263-a9f3-a5e1417cd91f
Ignatenko, Vera
0a791974-3a01-43bb-827b-ec30deead858
Boukhers, Zeyd
0768f27b-2434-442a-bf16-00264e90b3cd
Staab, Steffen
bf48d51b-bd11-4d58-8e1c-4e6e03b30c49
Koltsov, Sergei
022812a6-4063-4263-a9f3-a5e1417cd91f
Ignatenko, Vera
0a791974-3a01-43bb-827b-ec30deead858
Boukhers, Zeyd
0768f27b-2434-442a-bf16-00264e90b3cd
Staab, Steffen
bf48d51b-bd11-4d58-8e1c-4e6e03b30c49

Koltsov, Sergei, Ignatenko, Vera, Boukhers, Zeyd and Staab, Steffen (2020) Analyzing the influence of hyper-parameters and regularizers of topic modeling in terms of Renyi entropy. Entropy, 22 (4), 394. (doi:10.3390/e22040394).

Record type: Article

Abstract

Topic modeling is a popular technique for clustering large collections of text documents. A variety of different types of regularization is implemented in topic modeling. In this paper, we propose a novel approach for analyzing the influence of different regularization types on results of topic modeling. Based on Renyi entropy, this approach is inspired by the concepts from statistical physics, where an inferred topical structure of a collection can be considered an information statistical system residing in a non-equilibrium state. By testing our approach on four models—Probabilistic Latent Semantic Analysis (pLSA), Additive Regularization of Topic Models (BigARTM), Latent Dirichlet Allocation (LDA) with Gibbs sampling, LDA with variational inference (VLDA)—we, first of all, show that the minimum of Renyi entropy coincides with the “true” number of topics, as determined in two labelled collections. Simultaneously, we find that Hierarchical Dirichlet Process (HDP) model as a well-known approach for topic number optimization fails to detect such optimum. Next, we demonstrate that large values of the regularization coefficient in BigARTM significantly shift the minimum of entropy from the topic number optimum, which effect is not observed for hyper-parameters in LDA with Gibbs sampling. We conclude that regularization may introduce unpredictable distortions into topic models that need further research.

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More information

Accepted/In Press date: 25 March 2020
e-pub ahead of print date: 30 March 2020

Identifiers

Local EPrints ID: 438982
URI: http://eprints.soton.ac.uk/id/eprint/438982
PURE UUID: e8b5c7a9-b3d9-44e1-8f3f-fa22ae3ec1a6
ORCID for Steffen Staab: ORCID iD orcid.org/0000-0002-0780-4154

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Date deposited: 31 Mar 2020 16:30
Last modified: 07 Oct 2020 02:07

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Contributors

Author: Sergei Koltsov
Author: Vera Ignatenko
Author: Zeyd Boukhers
Author: Steffen Staab ORCID iD

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