Multilingual Document Clustering: An Heuristic Approach Based on Cognate Named Entities

Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 1145–1152, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Multilingual Document Clustering: an Heuristic Approach Based on Cognate Named Entities Soto Montalvo GAVAB Group URJC soto.montalvo@urjc.es Raquel Mart´ınez NLP&IR Group UNED raquel@lsi.uned.es Arantza Casillas Dpt. EE UPV-EHU arantza.casillas@ehu.es V´ıctor Fresno GAVAB Group URJC victor.fresno@urjc.es Abstract This paper presents an approach for Mul- tilingual Document Clustering in compa- rable corpora. The algorithm is of heuris- tic nature and it uses as unique evidence for clustering the identification of cognate named entities between both sides of the comparable corpora. One of the main ad- vantages of this approach is that it does not depend on bilingual or multilingual re- sources. However, it depends on the pos- sibility of identifying cognate named enti- ties between the languages used in the cor- pus. An additional advantage of the ap- proach is that it does not need any infor- mation about the right number of clusters; the algorithm calculates it. We have tested this approach with a comparable corpus of news written in English and Spanish. In addition, we have compared the results with a system which translates selected document features. The obtained results are encouraging. 1 Introduction Multilingual Document Clustering (MDC) in- volves dividing a set of n documents, written in different languages, into a specified number k of clusters, so the documents that are similar to other documents are in the same cluster. Meanwhile a multilingual cluster is composed of documents written in different languages, a monolingual clus- ter is composed of documents written in one lan- guage. MDC has many applications. The increasing amount of documents written in different lan- guages that are available electronically, leads to develop applications to manage that amount of information for filtering, retrieving and grouping multilingual documents. MDC tools can make easier tasks such as Cross-Lingual Information Retrieval, the training of parameters in statistics based machine translation, or the alignment of par- allel and non parallel corpora, among others. MDC systems have developed different solu- tions to group related documents. The strate- gies employed can be classified in two main groups: the ones which use translation technolo- gies, and the ones that transform the document into a language-independent representation. One of the crucial issues regarding the methods based on document or features translation is the correctness of the proper translation. Bilingual re- sources usually suggest more than one sense for a source word and it is not a trivial task to select the appropriate one. Although word-sense disam- biguation methods can be applied, these are not free of errors. On the other hand, methods based on language-independent representation also have limitations. For instance, those based on thesaurus depend on the thesaurus scope. Numbers or dates identification can be appropriate for some types of clustering and documents; however, for other types of documents or clustering it could not be so relevant and even it could be a source of noise. In …

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