Robust Semi-supervised and Ensemble-Based Methods in Word Sense Disambiguation

  • Anders Søgaard
  • Anders Johannsen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6233)

Abstract

Mihalcea [1] discusses self-training and co-training in the context of word sense disambiguation and shows that parameter optimization on individual words was important to obtain good results. Using smoothed co-training of a naive Bayes classifier she obtains a 9.8% error reduction on Senseval-2 data with a fixed parameter setting. In this paper we test a semi-supervised learning algorithm with no parameters, namely tri-training [2]. We also test the random subspace method [3] for building committees out of stable learners. Both techniques lead to significant error reductions with different learning algorithms, but improvements do not accumulate. Our best error reduction is 7.4%, and our best absolute average over Senseval-2 data, though not directly comparable, is 12% higher than the results reported in Mihalcea [1].

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Mihalcea, R.: Co-training and self-training for word sense disambiguation. In: CONLL, Boston, MA (2004)Google Scholar
  2. 2.
    Li, M., Zhou, Z.H.: Tri-training: exploiting unlabeled data using three classifiers. IEEE Transactions on Knowledge and Data Engineering 17(11), 1529–1541 (2005)CrossRefGoogle Scholar
  3. 3.
    Ho, T.K.: The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8), 832–844 (1998)CrossRefGoogle Scholar
  4. 4.
    Abney, S.: Semi-supervised learning for computational linguistics. Chapman and Hall, Boca Raton (2008)Google Scholar
  5. 5.
    Chen, W., Zhang, Y., Isahara, H.: Chinese chunking with tri-training learning. In: Matsumoto, Y., Sproat, R.W., Wong, K.-F., Zhang, M. (eds.) ICCPOL 2006. LNCS (LNAI), vol. 4285, pp. 466–473. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Nguyen, T., Nguyen, L., Shimazu, A.: Using semi-supervised learning for question classification. Journal of Natural Language Processing 15, 3–21 (2008)Google Scholar
  7. 7.
    García-Pedrajas, N., Ortiz-Boyer, D.: Boosting random subspace method. Neural Networks 21(9), 1344–1362 (2008)CrossRefGoogle Scholar
  8. 8.
    Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Annals of Statistics 28(2), 337–407 (2000)MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Frank, E., Witten, I.: Generating accurate rule sets without global optimization. In: The 15th International Conference on Machine Learning (1995)Google Scholar
  10. 10.
    Sindhwani, V., Keerthi, S.: Large scale semi-supervised linear SVMs. In: ACM SIGIR, Seattle, WA (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Anders Søgaard
    • 1
  • Anders Johannsen
    • 1
  1. 1.Centre for Language TechnologyUniversity of CopenhagenCopenhagen S

Personalised recommendations