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:
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.lrec-1.833.pdf