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A Probabilistic Rating System for Team Competitions with Individual Contributions Read chapter Read first chapter

2015 | OriginalPaper | Chapter

BigARTM: Open Source Library for Regularized Multimodal Topic Modeling of Large Collections

Authors : Konstantin Vorontsov, Oleksandr Frei, Murat Apishev, Peter Romov, Marina Dudarenko

Published in: Analysis of Images, Social Networks and Texts

Publisher: Springer International Publishing

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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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Footnotes
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Metadata
Title
BigARTM: Open Source Library for Regularized Multimodal Topic Modeling of Large Collections
Authors
Konstantin Vorontsov
Oleksandr Frei
Murat Apishev
Peter Romov
Marina Dudarenko
Copyright Year
2015
Book
Analysis of Images, Social Networks and Texts

Print ISBN: 978-3-319-26122-5

Electronic ISBN: 978-3-319-26123-2

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-26123-2_36

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