TopicNet: Making Additive Regularisation for Topic Modelling Accessible

Victor Bulatov, Vasiliy Alekseev, Konstantin Vorontsov, Darya Polyudova, Eugenia Veselova, Alexey Goncharov, Evgeny Egorov


Abstract
This paper introduces TopicNet, a new Python module for topic modeling. This package, distributed under the MIT license, focuses on bringing additive regularization topic modelling (ARTM) to non-specialists using a general-purpose high-level language. The module features include powerful model visualization techniques, various training strategies, semi-automated model selection, support for user-defined goal metrics, and a modular approach to topic model training. Source code and documentation are available at https://github.com/machine-intelligence-laboratory/TopicNet
Anthology ID:
2020.lrec-1.833
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
6745–6752
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.833
DOI:
Bibkey:
Cite (ACL):
Victor Bulatov, Vasiliy Alekseev, Konstantin Vorontsov, Darya Polyudova, Eugenia Veselova, Alexey Goncharov, and Evgeny Egorov. 2020. TopicNet: Making Additive Regularisation for Topic Modelling Accessible. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 6745–6752, Marseille, France. European Language Resources Association.
Cite (Informal):
TopicNet: Making Additive Regularisation for Topic Modelling Accessible (Bulatov et al., LREC 2020)
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PDF:
https://aclanthology.org/2020.lrec-1.833.pdf