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Dependency Analysis of Clauses Using Parse Tree Kernels

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Computational Linguistics and Intelligent Text Processing (CICLing 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4394))

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Abstract

Identification of dependency relation among clauses is one of the most critical parts in parsing Korean sentences because it generates severe ambiguities. The resolution of the ambiguities involves both syntactic and semantic information. This paper proposes a method to determine the dependency relation among Korean clauses using parse tree kernels. The parse tree used in this paper provides the method with the syntactic information, and the endings (Eomi) do with the semantic information. In addition, the parse tree kernel for handling parse trees has benefits that it minimizes the information loss occurred during transforming a parse tree into a feature vector, and can obtain, as a result, very accurate similarity between parse trees. The experimental results on a standard Korean data set show 89.12% of accuracy, which implies that the proposed method is plausible for the dependency analysis of clauses.

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References

  1. Bunescu, R., Mooney, R.: A Shortest Path Dependency Kernel for Relation Extraction. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 724–731 (2005)

    Google Scholar 

  2. Carreras, X., Màrquez, L.: Boosting Trees for Clause Splitting. In: Proceedings of the 5th Conference on Natural Language Learning, pp. 73–75 (2001)

    Google Scholar 

  3. Collins, M., Duffy, N.: Convolution Kernels for Natural Language. In: Proceedings of the 14th Neural Information Processing Systems (2001)

    Google Scholar 

  4. Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, Cambridge (2000)

    Book  MATH  Google Scholar 

  5. Joachims, T.: Text Categorization with Support Vector Machines: Learning with Many Relevant Features. In: Proceedings of the European Conference on Machine Learning, pp. 137–142 (1998)

    Google Scholar 

  6. Kang, S.-S.: A Dependency Parsing Method for Head-Final Languages. In: Proceedings of the 2001 IEEE International Symposium on Industrial Electronics, pp. 696–699. IEEE Press, Los Alamitos (2001)

    Google Scholar 

  7. Kudo, T., Matsumoto, Y.: Japanese Dependency Analyisis using Cascaded Chunking. In: Proceedings of the 6th Conference on Natural Language Learning, pp. 1–7 (2002)

    Google Scholar 

  8. Lee, H.-J., Park, S.-B., Lee, S.-J., Park, S.-Y: Clause Boundary Recognition Using Support Vector Machines. In: Proceedings of the 9th Pacific Rim International Conference on Artificial Intelligence, pp. 505–514 (2006)

    Google Scholar 

  9. Lee, S.-Z., Tsujii, J., Rim, H.-C.: Hidden Markov Model-Based Korean Part-of-Speech Tagging Considering High Agglutinativity, Word-Spacing, and Lexical Correlativity. In: Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics, pp. 376–383 (2000)

    Google Scholar 

  10. Moschitti, A.: A study on Convolution Kernels for Shallow Semantic Parsing. In: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics, pp. 335–342 (2004)

    Google Scholar 

  11. Park, S.-B., Zhang, B.-T.: Text Chunking by Combining Hand-Crafted Rules and Memory-Based Learning. In: Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics, pp. 497–504 (2003)

    Google Scholar 

  12. Park, S.-B., Tae, Y.-S., Park, S.-Y.: Self-Organizing n-gram Model for Automatic Word Spacing. In: Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, pp. 633–640 (2006)

    Google Scholar 

  13. Kim Sang, T., Erik, F., Déjean, H.: Introduction to the CoNLL-2001 Shared Task: Clause Identification. In: Proceedings of the 5th Conference on Natural Language Learning, pp. 53–57 (2001)

    Google Scholar 

  14. Zhang, M., Zhang, J., Su, J., Zhou, G.-D.: A Composite Kernel to Extract Relations between Entities with Both Flat and Structured Features. In: Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, pp. 825–832 (2006)

    Google Scholar 

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Alexander Gelbukh

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© 2007 Springer-Verlag Berlin Heidelberg

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Kim, SS., Park, SB., Lee, SJ. (2007). Dependency Analysis of Clauses Using Parse Tree Kernels. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2007. Lecture Notes in Computer Science, vol 4394. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70939-8_20

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  • DOI: https://doi.org/10.1007/978-3-540-70939-8_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-70938-1

  • Online ISBN: 978-3-540-70939-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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