Readitopics: Make your topic models readable via labeling and browsing

Velcin Julien, Gourru Antoine, Giry-Fouquet Erwan, Gravier Christophe, Roche Mathieu, Poncelet Pascal. 2018. Readitopics: Make your topic models readable via labeling and browsing. In : Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18). Lang Jérôme (ed.). IJCAI. Vienne : International Joint Conferences on Artificial Intelligence, 5874-5876. ISBN 978-0-9992411-2-7 International Joint Conference on Artificial Intelligence (IJCAI-18). 27, Stockholm, Suède, 13 Juillet 2018/19 Juillet 2018.

Url - éditeur : https://www.ijcai.org/proceedings/2018/

Résumé : Readitopics provides a new tool for browsing a textual corpus that showcases several recent work on topic labeling and topic coherence. We demonstrate the potential of these techniques to get a deeper understanding of the topics that structure different datasets. This tool is provided as a Web demo but it can be installed to experiment with your own dataset. It can be further extended to deal with more advanced topic modeling techniques.

Mots-clés libres : Clustering, Text mining, Information retrieval

Auteurs et affiliations

• Velcin Julien, Université de Lyon (FRA)
• Gourru Antoine, Université de Lyon (FRA)
• Giry-Fouquet Erwan, Université de Lyon (FRA)
• Gravier Christophe, Université de Lyon (FRA)
• Roche Mathieu, CIRAD-ES-UMR TETIS (FRA) ORCID: 0000-0003-3272-8568
• Poncelet Pascal, LIRMM (FRA)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/588316/)

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