Statistical methods for speech recognition
Statistical methods for speech recognition
The EuTrans Spoken Language Translation System
Machine Translation
Defense of the ansatz for dynamical hierarchies
Artificial Life
Learning Subsequential Transducers for Pattern Recognition Interpretation Tasks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improve the Learning of Subsequential Transducers by Using Alignments and Dictionaries
ICGI '00 Proceedings of the 5th International Colloquium on Grammatical Inference: Algorithms and Applications
Using domain information during the learning of a subsequential transducer
ICG! '96 Proceedings of the 3rd International Colloquium on Grammatical Inference: Learning Syntax from Sentences
Translation with Finite-State Devices
AMTA '98 Proceedings of the Third Conference of the Association for Machine Translation in the Americas on Machine Translation and the Information Soup
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
An empirical study of smoothing techniques for language modeling
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Improved statistical alignment models
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
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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.