Shallow Text Clustering Does Not Mean Weak Topics: How Topic Identification Can Leverage Bigram Features

Julien Velcin 1 Mathieu Roche 2, 3 Pascal Poncelet 2
1 ERIC - Entrepôts, Représentation et Ingénierie des Connaissances
2 ADVANSE - ADVanced Analytics for data SciencE
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
3 UMR TETIS - Territoires, Environnement, Télédétection et Information Spatiale
Abstract : Text clustering and topic learning are two closely related tasks. In this paper, we show that the topics can be learnt without the absolute need of an exact categorization. In particular, the experiments performed on two real case studies with a vocabulary based on bigram features lead to extracting readable topics that cover most of the documents. Precision at 10 is up to 74% for a dataset of scientific abstracts with 10,000 features, which is 4% less than when using unigrams only but provides more interpretable topics.
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Communication dans un congrès
DMNLP: Data Mining and Natural Language Processing, Sep 2016, Riva del Garda, Italy. 3rd Workshop on Interactions between Data Mining and Natural Language Processing 2016 co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2016), 1646, 2016, 〈http://ceur-ws.org/Vol-1646/〉
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Dernière modification le : mercredi 31 octobre 2018 - 12:24:20
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Julien Velcin, Mathieu Roche, Pascal Poncelet. Shallow Text Clustering Does Not Mean Weak Topics: How Topic Identification Can Leverage Bigram Features. DMNLP: Data Mining and Natural Language Processing, Sep 2016, Riva del Garda, Italy. 3rd Workshop on Interactions between Data Mining and Natural Language Processing 2016 co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2016), 1646, 2016, 〈http://ceur-ws.org/Vol-1646/〉. 〈lirmm-01362434〉

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