@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",
booktitle = "Proceedings of the 12th 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 https://github.com/machine-intelligence-laboratory/TopicNet",
language = "English",
ISBN = "979-10-95546-34-4",
}
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<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</abstract>
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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
%S Proceedings of the 12th 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)
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 12th Language Resources and Evaluation Conference, pages 6745–6752, Marseille, France. European Language Resources Association.