Advertisement

Detection of Error-Prone Cases for Word Sense Disambiguation

  • Yong-Jin Han
  • Sang-Jo Lee
  • Se Young Park
  • Seong-Bae Park
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8226)

Abstract

Word sense disambiguation (WSD) is essential for natural language understanding applications such as machine translation, question & answering, and natural language interface, since the performance of such applications depends on the senses of lexicons. Thus, it is natural to consider lexicons as the most crucial features in WSD. However, due to the extensiveness of lexical space, WSD methods based on machine learning techniques with lexical features suffer from the sparse data problem. To tackle this problem, this paper proposes a hybrid approach which separately copes with an error-prone data due to sparsity. A data is regarded as error-prone if its nearest neighbors are relatively distant and their senses are uniformly distributed. Then, our hybrid approach focuses on such an error-prone data without tuning of a base method. In the experiments, the k-nearest neighbor method is used as a base method. If a data is determined as an error-prone case, it is processed by a prototype based method. The prototype based method takes an advantage from overall training examples rather than depends on only several neighbors. The experimental results on Senseval-3 nouns show that an error-prone data is effectively detected by the proposed method and our hybrid approach outperforms the ordinary k-nearest neighbor method and the prototype based method.

Keywords

Word Sense Disambiguation Error-prone case Data Sparseness 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Mihalcea, R., Chklovski, T., Kilgarriff, A.: The senseval-3 english lexical sample task. In: Senseval-3: Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, Barcelona, Spain, pp. 25–28 (2004)Google Scholar
  2. 2.
    Francis, W.N., Kucera, H.: Computational analysis of present-day american english (1967)Google Scholar
  3. 3.
    Agirre, E., de Lacalle, O.L., Martınez, D.: Exploring feature spaces with svd and unlabeled data for word sense disambiguation. In: Proceedings of RANLP (2005)Google Scholar
  4. 4.
    Barnett, V., Lewis, T.: Outliers in statistical data, vol. 3. Wiley, New York (1994)zbMATHGoogle Scholar
  5. 5.
    Gliozzo, A., Giuliano, C., Strapparava, C.: Domain kernels for word sense disambiguation. In: Proceedings of ACL, pp. 403–410. ACL (2005)Google Scholar
  6. 6.
    Aggarwal, C.C., Yu, P.S.: Outlier detection for high dimensional data. In: ACM Sigmod Record, vol. 30, pp. 37–46. ACM (2001)Google Scholar
  7. 7.
    Hodge, V.J., Austin, J.: A survey of outlier detection methodologies. Artificial Intelligence Review 22(2), 85–126 (2004)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yong-Jin Han
    • 1
  • Sang-Jo Lee
    • 1
  • Se Young Park
    • 1
  • Seong-Bae Park
    • 1
  1. 1.Kyungpook National UniversityDaeguKorea

Personalised recommendations