Comparing Two Markov Methods for Part-of-Speech Tagging of Portuguese

  • Fábio N. Kepler
  • Marcelo Finger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4140)


There is a wide variety of statistical methods applied to Part-of-Speech (PoS) tagging, that associate words in a text to their corresponding PoS. The majority of those methods analyse a fixed, small neighborhood of words imposing some form of Markov restriction. In this work we implement and compare a fixed length hidden Markov model (HMM) with a variable length Markov chain (VLMC); the latter is, in principle, capable of detecting long distance dependencies. We show that the VLMC model performs better in terms of accuracy and almost equally in terms of tagging time, also doing very well in training time. However, the VLMC method actually fails to capture really long distance dependencies, and we analyse the reasons for such behaviour.


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  1. 1.
    Rabiner, L.R.: A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE 77(2), 257–285 (1989)CrossRefGoogle Scholar
  2. 2.
    Bühlmann, P., Wyner, A.J.: Variable length markov chains. Annals of Statistics 27(2), 480–513 (1999)MATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Mächler, M., Bühlmann, P.: Variable length markov chains: Methodology, computing and software. Research Report 104, Eidgenossische Technische Hochschule (ETH), CH-8091 Zürich, Switzerland (2002) Seminar fur StatistikGoogle Scholar
  4. 4.
    Rissanen, J.: A universal data compression system. IEEE Trans. Inform. Theory IT-29, 656–664 (1983)Google Scholar
  5. 5.
    IEL-UNICAMP and IME-USP: Corpus Anotado do Português Histórico Tycho Brahe, Acessado em 2005 (2005)Google Scholar
  6. 6.
    Church, K.W.: A stochastic parts program and noun phrase parser for unrestricted text. In: Proceedings of the second conference on Applied natural language processing, Austin, Texas, Association for Computational Linguistics, pp. 136–143 (1988)Google Scholar
  7. 7.
    DeRose, S.J.: Grammatical category disambiguation by statistical optimization. Computational Linguistics 14, 31–39 (1988)Google Scholar
  8. 8.
    Brill, E.: Unsupervised learning of disambiguation rules for part of speech tagging. In: Yarovsky, D., Church, K. (eds.) Proceedings of the Third Workshop on Very Large Corpora, Somerset, New Jersey, Association for Computational Linguistics, pp. 1–13 (1995)Google Scholar
  9. 9.
    Marcus, M.P., Santorini, B., Marcinkiewicz, M.A.: Building a large annotated corpus of english: The penn treebank. Computational Linguistics 19(2), 313–330 (1994)Google Scholar
  10. 10.
    Alves, C.D.C., Finger, M.: Etiquetagem do português clássico baseada em córpora. In: Proceedings of IV Encontro para o Processamento Computacional da Língua Portuguesa Escrita e Falada (PROPOR 1999), Évora, Portugal, pp. 21–22 (1999)Google Scholar
  11. 11.
    Finger, M.: Técnicas de otimização da precisão empregadas no etiquetador Tycho Brahe. In: Proceedings of V Encontro para o Processamento Computacional da Língua Portuguesa Escrita e Falada (PROPOR 2000), Atibaia, Brazil, pp. 19–22 (2000)Google Scholar
  12. 12.
    Ratnaparkhi, A.: A maximum entropy model for part-of-speech tagging. In: Proceedings of the Empirical Methods in Natural Language Processing Conference, University of Pennsylvania (1996)Google Scholar
  13. 13.
    Brants, T.: Tnt – a statistical part-of-speech tagger. In: Proceedings of the Sixth Applied Natural Language Processing Conference (ANLP-2000), Seattle, WA (2000)Google Scholar
  14. 14.
    Toutanova, K., Klein, D., Manning, C.D., Singer, Y.: Feature-rich part-of-speech tagging with a cyclic dependency network. In: Proceedings of HLT-NAACL 2003, pp. 252–259 (2003)Google Scholar
  15. 15.
    Aires, R.V.X.: Implementação, adaptação, combinação e avaliação de etiquetadores para o português do brasil. Dissertação de mestrado, Instituto de Ciências Matemáticas e Computação, Universidade de São Paulo - Campus São Carlos (2000)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Fábio N. Kepler
    • 1
  • Marcelo Finger
    • 1
  1. 1.Institute of Mathematics and StatisticsUniversity of São Paulo (USP) 

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