Different Approaches to Bilingual Text Classification Based on Grammatical Inference Techniques

  • Jorge Civera
  • Elsa Cubel
  • Alfons Juan
  • Enrique Vidal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3523)


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.


Machine Translation Input Sentence Grammatical Inference Regular Grammar Bilingual Corpus 
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  1. 1.
    McCallum, A., Nigam, K.: A comparison of event models for naive bayes text classification. In: AAAI 1998 Workshop on Learning for Text Categorization (1998)Google Scholar
  2. 2.
    Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  3. 3.
    Yang, Y.: An evaluation of statistical approaches to text categorization. Information Retrieval 1, 69–90 (1999)CrossRefGoogle Scholar
  4. 4.
    Picó, D., Casacuberta, F.: Some statistical-estimation methods for stochastic finitestate transducers. Machine Learning 44, 121–142 (2001)zbMATHCrossRefGoogle Scholar
  5. 5.
    Knight, K., Al-Onaizan, Y.: Translation with finite-state devices. In: Farwell, D., Gerber, L., Hovy, E. (eds.) AMTA 1998. LNCS (LNAI), vol. 1529, pp. 421–437. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  6. 6.
    Vidal, E.: Finite-state speech-to-speech translation. In: Int. Conf. on Acoustics Speech and Signal Processing, Munich, Germany, vol. 1, pp. 111–114 (1997)Google Scholar
  7. 7.
    Amengual, J.C., Benedí, J.M., Castano, A., Castellanos, A., Jiménez, V.M., Llorens, D., Marzal, A., Pastor, M., Prat, F., Vidal, E., Vilar, J.M.: The EuTrans-I speech translation system. Machine Translation 15, 75–103 (2000)zbMATHCrossRefGoogle Scholar
  8. 8.
    Oncina, J., García, P., Vidal, E.: Learning subsequential transducers for pattern recognition interpretation tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence 15, 448–458 (1993)CrossRefGoogle Scholar
  9. 9.
    Gold, E.M.: Language identification in the limit. Information and Control 10, 447–474 (1967)zbMATHCrossRefGoogle Scholar
  10. 10.
    Oncina, J., Varó, M.A.: Using domain information during the learning of a subsequential transducer. In: ICGI, Berlin, Germany, pp. 301–312 (1996)Google Scholar
  11. 11.
    Cubel, E.: Aprendizaje de transductores subsecuenciales estocásticos. Technical Report II-DSIC-B-23/01, Universidad Politécnica de Valencia, Spain (2002)Google Scholar
  12. 12.
    Och, F.J., Ney, H.: Improved statistical alignment models. In: ACL 2000, Hong Kong, China, pp. 440–447 (2000)Google Scholar
  13. 13.
    Brown, P.F., Pietra, S.D., Pietra, V.J.D., Mercer, R.L.: The mathematics of statistical machine translation: Parameter estimation. Computational Linguistics 19, 263–312 (1993)Google Scholar
  14. 14.
    Viterbi, A.: Error bounds for convolutional codes and a asymtotically optimal decoding algorithm. IEEE Transactions on Information Theory 13, 260–269 (1967)zbMATHCrossRefGoogle Scholar
  15. 15.
    Witten, I.H., Bell, T.C.: The zero-frequency problem: Estimating the probabilities of novel events in adaptive text compression. IEEE Trans. Information Theory 37, 1085–1094 (1991)CrossRefGoogle Scholar
  16. 16.
    Chen, S.F., Goodman, J.: An empirical study of smoothing techniques for language modelling. In: Proceedings of the 34th Annual Meeting of the Association for Computational Linguistics, San Francisco, USA, pp. 310–318 (1996)Google Scholar
  17. 17.
    Juan, A., Vidal, E.: On the use of bernoulli mixture models for text classification. In: Workshop on Pattern Recognition in Information Systems (PRIS 2001), Setúbal, Portugal (2001)Google Scholar
  18. 18.
    Llorens, D.: Suavizado de autómatas y traductores finitos estocásticos. PhD thesis, Universitat Politècnica de València (2000), Advisor(s): Dr. J. M. Vilar and Dr. F. CasacubertaGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Jorge Civera
    • 1
  • Elsa Cubel
    • 2
  • Alfons Juan
    • 1
  • Enrique Vidal
    • 2
  1. 1.Departamento de Sistemas Informáticos y ComputaciónUniversidad Politécnica de Valencia 
  2. 2.Instituto Tecnológico de InformáticaUniversidad Politécnica de Valencia 

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