Cross-Lingual Document Similarity Calculation Using the Multilingual Thesaurus EUROVOC
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
A multilingual news summarizer
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Multilingual document clustering: an heuristic approach based on cognate named entities
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Multilingual and cross-lingual news topic tracking
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Cross language text categorization by acquiring multilingual domain models from comparable corpora
ParaText '05 Proceedings of the ACL Workshop on Building and Using Parallel Texts
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This paper presents an approach for Multilingual News Document Clustering in comparable corpora We have implemented two algorithms of heuristic nature that follow the approach They use as unique evidence for clustering the identification of cognate named entities between both sides of the comparable corpora In addition, no information about the right number of clusters has to be provided to the algorithms The applicability of the approach only depends on the possibility of identifying cognate named entities between the languages involved in the corpus The main difference between the two algorithms consists of whether a monolingual clustering phase is applied at first or not We have tested both algorithms with a comparable corpus of news written in English and Spanish The performance of both algorithms is slightly different; the one that does not apply the monolingual phase reaches better results In any case, the obtained results with both algorithms are encouraging and show that the use of cognate named entities can be enough knowledge for deal with multilingual clustering of news documents.