Semantic-based multilingual document clustering via tensor modeling
2014
Romeo, S. | Tagarelli, A. | Ienco, Dino | Dipartimento di Ingegneria Informatica, Modellistica, Elettronica e Sistemistica [Calabria] (DIMES) ; Università della Calabria [Arcavacata di Rende, Italia] = University of Calabria [Italy] = Université de Calabre [Italie] (UniCal) | Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)
[Departement_IRSTEA]Territoires [TR1_IRSTEA]SYNERGIE [Axe_IRSTEA]TETIS-SISO<br/>EMNLP, Conference on Empirical Methods in Natural Language Processing , Doha, QAT, 25-/10/2014 - 29/10/2014
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Show more [+] Less [-]English. A major challenge in document clustering research arises from the growing amount of text data written in different languages. Previous approaches depend on language-specific solutions (e.g., bilingual dictionaries, sequential machine translation) to evaluate document similarities, and the required transformations may alter the original document semantics. To cope with this issue we propose a new document clustering approach for multilingual corpora that (i) exploits a large-scale multilingual knowledge base, (ii) takes advantage of the multi-topic nature of the text documents, and (iii) employs a tensor-based model to deal with high dimensionality and sparseness. Results have shown the significance of our approach and its better performance w.r.t. classic document clustering approaches, in both a balanced and an unbalanced corpus evaluation.
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