Abstract
In this paper, we mainly explore the effectiveness of two kernel-based methods, the convolution tree kernel and the shortest path dependency kernel, in which parsing information is directly applied to Chinese relation extraction on ACE 2007 corpus. Specifically, we explore the effect of different parse tree spans involved in convolution kernel for relation extraction. Besides, we experiment with composite kernels by combining the convolution kernel with feature-based kernels to study the complementary effects between tree kernel and flat kernels. For the shortest path dependency kernel, we improve it by replacing the strict same length requirement with finding the longest common subsequences between two shortest dependency paths. Experiments show kernel-based methods are effective for Chinese relation extraction.
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References
MUC (1987-1998), http://www.itl.nist.gov/iaui/894.02/related_projects/muc/
ACE (2002-2007), urlhttp://projects.ldc.upenn.edu/ace/
Nanda, K.: Combining lexical, syntactic and emantic features with Maximum Entropy models for extracting relations. In: ACL 2004 (poster)
Zhou, G.D., Su, J., Zhang, J., Zhang, M.: Exploring Various Knowledge in Relation Extraction. In: ACL 2005 (2005)
Shubin, Z., Ralph, G.: Extracting Relations with Integrated Information Using Kernel Methods. In: ACL 2005 (2005)
Jiang, J., Zhai, C.: A systematic exploration of the feature space for relation extraction. NAACL-HLT (2007)
Haussler, D.: Convolution Kernels on Discrete Structures. Technical Report UCS-CRL-99-10, University of California, Santa Cruz (1999)
Schölkopf, B., Smola, A.J.: Learning with Kernels: SVM, Regularization, Optimization and Beyond, pp. 407–423. MIT Press, Cambridge (2001)
Collins, M., Duffy, N.: Convolution Kernels for Natural Language. In: NIPS (2001)
Collins, M., Duffy, N.: New ranking algorithms for parsing and tagging: Kernels over discrete structures, and the voted perceptron. In: ACL 2002(2002)
Zelenko, D., Aone, C., Richardella, A.: Kernel Methods for Relation Extraction. Journal of Machine Learning Research 2, 1083–1106 (2003)
Culotta, A., Sorensen, J.: Dependency Tree Kernel for Relation Extraction. In: ACL 2004 (2004)
Bunescu, R.C., Mooney, R.J.: A Shortest Path Dependency Kernel for Relation Extraction. In: EMNLP 2005 (2005)
Zhang, M., Zhang, J., Su, J.: Exploring syntactic features for relation extraction using a convolution tree kernel. In: Proceedings of HLT/NAACL (2006a)
Zhang, M., Zhang, J., Su, J., Zhou, G.D.: A Composite Kernel to Extract Relations between Entities with both Flat and Structured Features. In: COLINGACL (2006b)
Zhou, G.D., Zhang, M., Ji, D.H., Zhu, Q.M.: Tree Kernel-based Relation Extraction with Context-Sensitive Structured Parse Tree Information. In: ACL 2007 (2007)
Che, W.X.: Automatic Entity Relation Extraction. Journal of Chinese Information Processing 19(2) (2004)
Zhang, S.X.: Study about automatic entity relation extraction. Journal of Harbin Engineering University (July 2006)
Che, W.X.: Improved-Edit-Distance Kernel for Chinese Relation Extraction. In: Dale, R., Wong, K.-F., Su, J., Kwong, O.Y. (eds.) IJCNLP 2005. LNCS (LNAI), vol. 3651, Springer, Heidelberg (2005)
Moschitti. Convolution Tree kernel, http://ai-nlp.info.uniroma2.it/moschitti/TK1.2-software/Tree-Kernel.htm
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Huang, R., Sun, L., Feng, Y. (2008). Study of Kernel-Based Methods for Chinese Relation Extraction. In: Li, H., Liu, T., Ma, WY., Sakai, T., Wong, KF., Zhou, G. (eds) Information Retrieval Technology. AIRS 2008. Lecture Notes in Computer Science, vol 4993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68636-1_70
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DOI: https://doi.org/10.1007/978-3-540-68636-1_70
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