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Semi-supervised Learning for Portuguese Noun Phrase Extraction

  • Ruy Milidiú
  • Cicero Santos
  • Julio Duarte
  • Raúl Rentería
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3960)

Abstract

Semi-supervised learning is frequently used when we have a small labeled training set but a large set of unlabeled samples. In this paper, we combine Hidden Markov Models and Transformation Based Learning in a semi-supervised learning approach. Self-training and Co-training are the two semi-supervised techniques that we apply to our scheme in order to classify Portuguese noun phrases. Our main goal here is to show that we can achieve effective noun phrase extraction using fewer tagged examples by applying a semi-supervised technique. Our models show good improvement with a small labeled corpus and little with a large one.

Keywords

Noun Phrase Good Improvement Unlabeled Sample Small Corpus Supervise Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Miorelli, S.T.: Extra¸cão do sintagma nominal em senten¸cas em português. Master’s thesis, Pontifícia Universidade Católica, Porto Alegre - RS (2001)Google Scholar
  2. 2.
    Santos, C.N.: Aprendizado de máquina na identifica¸cão de sintagmas nominais: o caso do português brasileiro. Master’s thesis, IME, Rio de Janeiro - RJ (2005)Google Scholar
  3. 3.
    Pierce, D., Cardie, C.: Limitations of co-training for natural language learning from large datasets. In: Proceedings of the EMNLP (2001)Google Scholar
  4. 4.
    Freitas, M.C., Garrão, M., Oliveira, C., Santos, C.N., Silveira, M.: A anota¸cão de um corpus para o aprendizado supervisionado de um modelo de sn. In: Proceedings of the III TIL / XXV Congresso da SBC, São Leopoldo - RS (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ruy Milidiú
    • 1
  • Cicero Santos
    • 1
  • Julio Duarte
    • 2
  • Raúl Rentería
    • 1
  1. 1.Departamento de InformáticaPontifícia Universidade CatólicaRio de JaneiroBrazil
  2. 2.Centro Tecnológico do ExércitoRio de JaneiroBrazil

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