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An Edit Distance Approach to Shallow Semantic Labeling

  • Samuel W. K. Chan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4881)

Abstract

This paper proposes a model of semantic labeling based on the edit distance. The dynamic programming approach stresses on a non-exact string matching technique that takes full advantage of the underlying grammatical structure of 65,000 parse trees in a Treebank. Both part-of-speech and lexical similarity serve to identify the possible semantic labels, without miring into a pure linguistic analysis. The model described has been implemented. We also analyze the tradeoffs between the part-of-speech and lexical similarity in the semantic labeling. Experimental results for recognizing various labels in 10,000 sentences are used to justify its significances.

Keywords

Edit Distance Parse Tree Edit Operation Semantic Label Countable Noun 
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. Abney, S.: Parsing by chunks. In: Berwick, R., Abney, S., Tenny, C. (eds.) Principle-Based Parsing, Kluwer Academic, Dordrecht (1991)Google Scholar
  2. Carreras, X., Màrquez, L.: Introduction to the CoNLL-2005 shared task: Semantic role labeling. In: CoNLL. Proceedings of the 9th Conference on Computational Natural Language Learning, pp. 152–164 (2005)Google Scholar
  3. Chen, K.-J., Huang, C.-R., Chang, L.-P., Hsu, H.-L.: Sinica Corpus: Design Methodology for Balanced Corpora. In: PACLIC II. Proceedings of the 11th Pacific Asia Conference on Language, Information, and Computation, Seoul Korea, pp. 167–176 (1996)Google Scholar
  4. Church, K.: A stochastic parts program and noun phrase parser for unrestricted text. In: Proceedings of Second Conference on Applied Natural Language Processing, Austin, Texas (1988)Google Scholar
  5. CKIP. Sinica Chinese Treebank: An Introduction of Design Methodology. Academic Sinica (2004) Google Scholar
  6. Fillmore, C.J.: The case for case. In: Bach, E., Harms, R.T. (eds.) Universals in Linguistic Theory, pp. 1–90. Rinehart & Winston, Holt (1968)Google Scholar
  7. Haghighi, A., Toutanova, K., Manning, C.: A joint model for semantic role labeling. In: CoNLL. Proceedings of the 9th Conference on Computational Natural Language Learning (2005)Google Scholar
  8. Manning, C.D., Schutze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999)zbMATHGoogle Scholar
  9. Màrquez, L., Comas, P.R., Giménez, J., Català, N.: Semantic role labeling as sequential tagging. In: CoNLL. Proceedings of the 9th Conference on Computational Natural Language Learning (2005)Google Scholar
  10. Pradhan, S., Hacioglu, K., Ward, W., Martin, J.H., Jurafsky, D.: Semantic role chunking combining complementary syntactic views. In: CoNLL. Proceedings of the 9th Conference on Computational Natural Language Learning (2005)Google Scholar
  11. Punyakanok, V., Koomen, P., Roth, D., Yih, W.: Generalized inference with multiple semantic role labeling systems. In: CoNLL. Proceedings of the 9th Conference on Computational Natural Language Learning (2005)Google Scholar
  12. Ramshaw, L.A., Marcus, M.P.: Text chunking using transformation-based learning. In: Proceedings of the Third Workshop on Very Large Corpora, pp. 82–94 (1995)Google Scholar
  13. Surdeanu, M., Turmo, J.: Semantic role labeling using complete syntactic analysis. In: CoNLL. Proceedings of the 9th Conference on Computational Natural Language Learning (2005)Google Scholar
  14. Wagner, R.A., Fischer, M.J.: The string-to-string correction problem. Journal of the Association for Computing Machinery 21(1), 168–173 (1974)zbMATHMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Samuel W. K. Chan
    • 1
  1. 1.Dept. of Decision Sciences, The Chinese University of Hong Kong, Hong Kong SARChina

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