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A Cluster-Based Classification Approach to Semantic Role Labeling

  • Necati E. Ozgencil
  • Nancy McCracken
  • Kishan Mehrotra
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5027)

Abstract

In this paper, a new approach for multi-class classification problems is applied to the Semantic Role Labeling (SRL) problem, which is an important task for natural language processing systems to achieve better semantic understanding of text. The new approach applies to any classification problem with large feature sets. Data is partitioned using clusters on a subset of the features. A multi-label classifier is then trained individually on each cluster, using automatic feature selection to customize the larger feature set for the cluster. This algorithm is applied to the Semantic Role Labeling problem and achieves improvements in accuracy for both the argument identification classifier and the argument labeling classifier.

Keywords

Machine Learning Natural Language Processing Clustering Classification 

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References

  1. 1.
    Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2001), http://www.csie.ntu.edu.tw/libsvm
  2. 2.
    Pradhan, S., Ward, W., Hacioglu, K., Martin, J., Jurafsky, D.: Semantic role labeling using different syntactic views. In: ACL (2005)Google Scholar
  3. 3.
    Mehrotra, K., Mohan, C.K., Ranka, S.: Elements of Artificial Neural Networks. MIT Press, Cambridge (1996)Google Scholar
  4. 4.
    Vafaie, H., DeJong, K.: Robust feature selection algorithms. Proc. 5th Intl. Conf. on Tools with Artifical Intelligence, 356–363 (1993)Google Scholar
  5. 5.
    Ritthoff, O., Klinkenberg, R., Fischer, S., Mierswa, I.: A hybrid approach to feature selection and generation using an evolutionary algorithm. Technical Report CI-127/02, Collaborative Research Center 531, University of Dortmund, Dortmund, Germany (2002), ISSN (1433)-3325Google Scholar
  6. 6.
    Pradhan, S., Hacioglu, K., Krugler, V., Ward, W., Martin, J.H., Jurafsky, D.: Support vector learning for semantic argument classification. Machine Learning Journal, Special issue of Speech and Natural Language Processing 60(1-3), 11–39 (2005)Google Scholar
  7. 7.
    Palmer, M., Gildea, D., Kingsbury, P.: The proposition bank: A corpus annotated with semantic roles. Journal of Computational Linguistics 31(1), 71–106 (2005)CrossRefGoogle Scholar
  8. 8.
    Baker, C.F., Fillmore, C.J., Lowe, J.B.: The Berkeley FrameNet project. In: Proceedings of the COLING-ACL, Montreal, Canada (1998)Google Scholar
  9. 9.
    Carreras, X., Màrquez, L.: Introduction to the conll-2005 shared task: Semantic role labeling. In: Proceedings of the COLING-ACL, Montreal, Canada (2005)Google Scholar
  10. 10.
    SENSEVAL-3: Third international workshop on the evaluation of systems for the semantic analysis of text. Association for Computational Linguistics, Barcelona, Spain (2004)Google Scholar
  11. 11.
    Charniak, E.: A maximum-entropy-inspired parser. In: Proceedings of the first conference on North American chapter of the Association for Computational Linguistics, pp. 132–139. Morgan Kaufmann Publishers Inc., San Francisco (2000)Google Scholar
  12. 12.
    Gildea, D., Jurafsky, D.: Automatic labeling of semantic roles. Computational Linguistics 28(3), 245–288 (2002)CrossRefGoogle Scholar
  13. 13.
    Punyakanok, V., Roth, D., Yih, W., Zimak, D.: Semantic role labeling via integer linear programming inference. In: Proceedings of COLING-2004 (2004)Google Scholar
  14. 14.
    Roth, D.: Learning to resolve natural language ambiguities: A unified approach. In: Proceedings of AAAI, pp. 806–813 (2004)Google Scholar
  15. 15.
    Toutanova, K., Haghighi, A., Manning, C.D.: Joint learning improves semantic role labeling. In: Proceedings of the Association for Computational Linguistics 43rd annual meeting (ACL2005), Ann Arbor, MI (2005)Google Scholar
  16. 16.
    Carreras, X., Màrquez, L.: Introduction to the CoNLL-2005 shared task: Semantic role labeling. In: Proceedings of the Ninth Conference on Computational Natural Language Learning (CoNLL-2005), Ann Arbor, Michigan, June 2005, Association for Computational Linguistics (2005)Google Scholar
  17. 17.
    Koomen, P., Punyakanok, V., Roth, D., Yih, W.-t.: Generalized inference with multiple semantic role labeling systems. In: Proceedings of the Ninth Conference on Computational Natural Language Learning (CoNLL-2005), Ann Arbor, Michigan, June 2005, pp. 181–184. Association for Computational Linguistics (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Necati E. Ozgencil
    • 1
  • Nancy McCracken
    • 1
  • Kishan Mehrotra
    • 1
  1. 1.Syracuse UniversitySyracuse 

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