Readitopics: Make Your Topic Models Readable via Labeling and Browsing

Abstract : Readitopics provides a new tool for browsing a textual corpus that showcases several recent work for labeling topic models and estimating topic coherence. We will demonstrate the potential of these techniques to get a deeper understanding of the topics that structure different kinds of datasets. This tool is provided as a Web demo but it can be easily installed to experiment with your own dataset. It can be further extended to deal with more advanced topic modeling techniques.
Type de document :
Communication dans un congrès
IJCAI: International Joint Conference on Artificial Intelligence, Jul 2018, Stockholm, Sweden. 27th International Joint Conference on Artificial Intelligence, 2018, 〈https://www.ijcai-18.org〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01910611
Contributeur : Pascal Poncelet <>
Soumis le : jeudi 1 novembre 2018 - 15:18:14
Dernière modification le : lundi 5 novembre 2018 - 01:08:29

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readitopics2018.pdf
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  • HAL Id : lirmm-01910611, version 1

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Julien Velcin, Antoine Gourru, Erwan Giry-Fouquet, Christophe Gravier, Mathieu Roche, et al.. Readitopics: Make Your Topic Models Readable via Labeling and Browsing. IJCAI: International Joint Conference on Artificial Intelligence, Jul 2018, Stockholm, Sweden. 27th International Joint Conference on Artificial Intelligence, 2018, 〈https://www.ijcai-18.org〉. 〈lirmm-01910611〉

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