Vasiliy Alekseev
2020
TopicNet: Making Additive Regularisation for Topic Modelling Accessible
Victor Bulatov
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Vasiliy Alekseev
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Konstantin Vorontsov
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Darya Polyudova
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Eugenia Veselova
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Alexey Goncharov
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Evgeny Egorov
Proceedings of the Twelfth Language Resources and Evaluation Conference
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
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Co-authors
- Victor Bulatov 1
- Konstantin Vorontsov 1
- Darya Polyudova 1
- Eugenia Veselova 1
- Alexey Goncharov 1
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