Abstract
Bilingual documentation has become a common phenomenon in many official institutions and private companies. In this scenario, the categorization of bilingual text is a useful tool, that can be also applied in the machine translation field. To tackle this classification task, different approaches will be proposed. On the one hand, two finite-state transducer algorithms from the grammatical inference domain will be discussed. On the other hand, the well-known naive Bayes approximation will be presented along with a possible modelization based on n-gram language models. Experiments carried out on a bilingual corpus have demonstrated the adequacy of these methods and the relevance of a second information source in text classification, as supported by classification error rates. Relative reduction of 29% with respect to the best previous results on the monolingual version of the same task has been obtained.
Work supported by the “Agència Valenciana de Ciència i Tecnologia” under grant GRUPOS03/031 and the Spanish project TIC2003-08681-C02-02.
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Civera, J., Cubel, E., Juan, A., Vidal, E. (2005). Different Approaches to Bilingual Text Classification Based on Grammatical Inference Techniques. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds) Pattern Recognition and Image Analysis. IbPRIA 2005. Lecture Notes in Computer Science, vol 3523. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11492542_77
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DOI: https://doi.org/10.1007/11492542_77
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