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)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.lrec-1.833.pdf
Export citation
@inproceedings{bulatov-etal-2020-topicnet,
title = "{T}opic{N}et: Making Additive Regularisation for Topic Modelling Accessible",
author = "Bulatov, Victor and
Alekseev, Vasiliy and
Vorontsov, Konstantin and
Polyudova, Darya and
Veselova, Eugenia and
Goncharov, Alexey and
Egorov, Evgeny",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.833",
pages = "6745--6752",
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 \url{https://github.com/machine-intelligence-laboratory/TopicNet}",
language = "English",
ISBN = "979-10-95546-34-4",
}
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%0 Conference Proceedings %T TopicNet: Making Additive Regularisation for Topic Modelling Accessible %A Bulatov, Victor %A Alekseev, Vasiliy %A Vorontsov, Konstantin %A Polyudova, Darya %A Veselova, Eugenia %A Goncharov, Alexey %A Egorov, Evgeny %Y Calzolari, Nicoletta %Y Béchet, Frédéric %Y Blache, Philippe %Y Choukri, Khalid %Y Cieri, Christopher %Y Declerck, Thierry %Y Goggi, Sara %Y Isahara, Hitoshi %Y Maegaard, Bente %Y Mariani, Joseph %Y Mazo, Hélène %Y Moreno, Asuncion %Y Odijk, Jan %Y Piperidis, Stelios %S Proceedings of the Twelfth Language Resources and Evaluation Conference %D 2020 %8 May %I European Language Resources Association %C Marseille, France %@ 979-10-95546-34-4 %G English %F bulatov-etal-2020-topicnet %X 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 %U https://aclanthology.org/2020.lrec-1.833 %P 6745-6752
Markdown (Informal)
[TopicNet: Making Additive Regularisation for Topic Modelling Accessible](https://aclanthology.org/2020.lrec-1.833) (Bulatov et al., LREC 2020)
- TopicNet: Making Additive Regularisation for Topic Modelling Accessible (Bulatov et al., LREC 2020)
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.