Abstract
Bilingual documentation has become a common phenomenon in official institutions and private companies. In this scenario, the categorization of bilingual text is a useful tool. In this paper, different approaches will be proposed to tackle this bilingual classification task. On the one hand, three finite-state transducer algorithms from the grammatical inference framework will be presented. On the other hand, a naive combination of smoothed n-gram models will be introduced. To evaluate the performance of bilingual classifiers, two categorized bilingual corpora of different complexity were considered. Experiments in a limited-domain task show that all the models obtain similar results. However, results on a more open-domain task denote the supremacy of the naive approach.
Work supported by the EC (FEDER) and the Spanish MEC under grant TIN2006-15694-CO2-01 and the Consellería d’Empresa, Universitat i Ciència - Generalitat Valenciana under contract GV06/252 and the Ministerio de Educación y Ciencia.
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Civera, J., Cubel, E., Vidal, E. (2007). Bilingual Text Classification. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2007. Lecture Notes in Computer Science, vol 4477. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72847-4_35
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DOI: https://doi.org/10.1007/978-3-540-72847-4_35
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