Abstract
Probabilistic topic modeling of text collections is a powerful tool for statistical text analysis. In this paper we announce the BigARTM open source project (http://bigartm.org) for regularized multimodal topic modeling of large collections. Several experiments on Wikipedia corpus show that BigARTM performs faster and gives better perplexity comparing to other popular packages, such as Vowpal Wabbit and Gensim. We also demonstrate several unique BigARTM features, such as additive combination of regularizers, topic sparsing and decorrelation, multimodal and multilanguage modeling, which are not available in the other software packages for topic modeling.
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Acknowledgements
The work was supported by the Russian Foundation for Basic Research grants 14-07-00847, 14-07-00908, 14-07-31176 and by Skolkovo Institute of Science and Technology (project 081-R).
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Vorontsov, K., Frei, O., Apishev, M., Romov, P., Dudarenko, M. (2015). BigARTM: Open Source Library for Regularized Multimodal Topic Modeling of Large Collections. In: Khachay, M., Konstantinova, N., Panchenko, A., Ignatov, D., Labunets, V. (eds) Analysis of Images, Social Networks and Texts. AIST 2015. Communications in Computer and Information Science, vol 542. Springer, Cham. https://doi.org/10.1007/978-3-319-26123-2_36
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