Abstract
When training hidden-Markov-model-based part-of-speech (PoS) taggers involved in machine translation systems in an unsupervised manner the use of target-language information has proven to give better results than the standard Baum-Welch algorithm. The target-language-driven training algorithm proceeds by translating every possible PoS tag sequence resulting from the disambiguation of the words in each source-language text segment into the target language, and using a target-language model to estimate the likelihood of the translation of each possible disambiguation. The main disadvantage of this method is that the number of translations to perform grows exponentially with segment length, translation being the most time-consuming task. In this paper, we present a method that uses a priori knowledge obtained in an unsupervised manner to prune unlikely disambiguations in each text segment, so that the number of translations to be performed during training is reduced. The experimental results show that this new pruning method drastically reduces the amount of translations done during training (and, consequently, the time complexity of the algorithm) without degrading the tagging accuracy achieved.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77(2), 257–286 (1989)
Baum, L.E.: An inequality and associated maximization technique in statistical estimation of probabilistic functions of a Markov process. Inequalities 3, 1–8 (1972)
Sánchez-Martínez, F., Pérez-Ortiz, J.A., Forcada, M.L.: Exploring the use of target-language information to train the part-of-speech tagger of machine translation systems. In: Vicedo, J.L., Martínez-Barco, P., Muńoz, R., Saiz Noeda, M. (eds.) EsTAL 2004. LNCS (LNAI), vol. 3230, pp. 137–148. Springer, Heidelberg (2004)
Corbí-Bellot, A.M., Forcada, M.L., Ortiz-Rojas, S., Pérez-Ortiz, J.A., Ramírez-Sánchez, G., Sánchez-Martínez, F., Alegria, I., Mayor, A., Sarasola, K.: An open-source shallow-transfer machine translation engine for the Romance languages of Spain. In: Proceedings of the 10th European Associtation for Machine Translation Conference, Budapest, Hungary, pp. 79–86 (2005)
Cutting, D., Kupiec, J., Pedersen, J., Sibun, P.: A practical part-of-speech tagger. In: Third Conference on Applied Natural Language Processing. Association for Computational Linguistics. Proceedings of the Conference, Trento, Italia, pp. 133–140 (1992)
Manning, C.D., Schutze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999)
Gale, W.A., Church, K.W.: Poor estimates of context are worse than none. In: Proceedings of a workshop on Speech and natural language, pp. 283–287. Morgan Kaufmann, San Francisco (1990)
Armentano-Oller, C., Carrasco, R.C., CorbÍ-Bellot, A.M., Forcada, M.L., Ginestí-Rosell, M., Ortiz-Rojas, S., Pérez-Ortiz, J.A., Ramírez-Sánchez, G., Sánchez-Martínez, F., Scalco, M.A.: Open-source Portuguese-Spanish machine translation. In: Vieira, R., Quaresma, P., Nunes, M.d.G.V., Mamede, N.J., Oliveira, C., Dias, M.C. (eds.) PROPOR 2006. LNCS (LNAI), vol. 3960, pp. 50–59. Springer, Heidelberg (2006)
Kupiec, J.: Robust part-of-speech tagging using a hidden Markov model. Computer Speech and Language 6(3), 225–242 (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sánchez-Martínez, F., Pérez-Ortiz, J.A., Forcada, M.L. (2006). Speeding Up Target-Language Driven Part-of-Speech Tagger Training for Machine Translation. In: Gelbukh, A., Reyes-Garcia, C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. Lecture Notes in Computer Science(), vol 4293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11925231_81
Download citation
DOI: https://doi.org/10.1007/11925231_81
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49026-5
Online ISBN: 978-3-540-49058-6
eBook Packages: Computer ScienceComputer Science (R0)