Multilingual Documents Clustering Based on Closed Concepts Mining

  • Mohamed ChebelEmail author
  • Chiraz Latiri
  • Eric Gaussier
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9261)


The scarcity of bilingual and multilingual parallel corpora has prompted many researchers to accentuate the need for new methods to enhance the quality of comparable corpora. In this paper, we highlight the interest and usefulness of Formal Concept Analysis in multiligual document clustering to improve corpora comparability. We propose a statistical approach for clustering multiligual documents based on multilingual Closed Concepts Mining to partition the documents belonging to one or more collections, writing in more than one language, in a set of classes. Experimental evaluation was conducted on two collections and showed a significant improvement of comparability of the generated classes.



This work is partially funded by the DGRST-CNRS \(n\circ \) 14/R 1401 Franco-Tunisian project, entitled “Text mining for construction of bilingual lexicons and multilingual information retrieval”


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Research Laboratory LIPAH, Faculty of Sciences of TunisUniversity Tunis El ManarTunisTunisia
  2. 2.Research Laboratory LIG, AMA GroupUniversity Joseph Fourier (Grenoble I)GrenobleFrance

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