Gourru Antoine, Velcin Julien, Roche Mathieu, Gravier Christophe, Poncelet Pascal.
2018. United we stand: using multiple strategies for topic labeling.
In : Natural language processing and information systems: 23rd International Conference on Applications of Natural Language to Information Systems, NLDB 2018, Paris, France, June 13-15, 2018, Proceedings. Silberztein Max (ed.), Atigui Faten (ed.), Kornyshova Elena (ed.), Métais Elisabeth (ed.), Meziane Farid (ed.). CNAM
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Résumé : Topic labeling aims at providing a sound, possibly multi-words, label that depicts a topic drawn from a topic model. This is of the utmost practical interest in order to quickly grasp a topic informational content – the usual ranked list of words that maximizes a topic presents limitations for this task. In this paper, we introduce three new unsupervised n-gram topic labelers that achieve comparable results than the existing unsupervised topic labelers but following different assumptions. We demonstrate that combining topic labelers - even only two - makes it possible to target a 64% improvement with respect to single topic labeler approaches and therefore opens research in that direction. Finally, we introduce a fourth topic labeler that extracts representative sentences, using Dirichlet smoothing to add contextual information. This sentence-based labeler provides strong surrogate candidates when n-gram topic labelers fall short on providing relevant labels, leading up to 94% topic covering.
Mots-clés libres : Clustering, Topic Modeling, Text mining
Auteurs et affiliations
- Gourru Antoine, Université de Lyon (FRA)
- Velcin Julien, Université de Lyon (FRA)
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Roche Mathieu, CIRAD-ES-UMR TETIS (FRA)
ORCID: 0000-0003-3272-8568
- Gravier Christophe, Université Jean Monnet (FRA)
- Poncelet Pascal, LIRMM (FRA)
Autres liens de la publication
Source : Cirad-Agritrop (https://agritrop.cirad.fr/588239/)
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