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Kernel-based semantic relation detection and classification via enriched parse tree structure

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Abstract

This paper proposes a tree kernel method of semantic relation detection and classification (RDC) between named entities. It resolves two critical problems in previous tree kernel methods of RDC. First, a new tree kernel is presented to better capture the inherent structural information in a parse tree by enabling the standard convolution tree kernel with context-sensitiveness and approximate matching of sub-trees. Second, an enriched parse tree structure is proposed to well derive necessary structural information, e.g., proper latent annotations, from a parse tree. Evaluation on the ACE RDC corpora shows that both the new tree kernel and the enriched parse tree structure contribute significantly to RDC and our tree kernel method much outperforms the state-of-the-art ones.

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References

  1. Automatic Content Extraction. http://www.ldc.upenn.edu/Projects/ACE/, 2000-2007.

  2. Haussler D. Convolution kernels on discrete structures. Technical Report UCS-CRL-99-10, University of California, Santa Cruzca, USA, 1999.

  3. Vapnik V. Statistical Learning Theory. Chichester: Whiley, UK, 1998.

    MATH  Google Scholar 

  4. Kambhatla N. Combining lexical, syntactic and semantic features with Maximum Entropy models for extracting relations. In Proc. ACL 2004, Barcelona, Spain, Jul. 21-26, 2004, pp.178-181.

  5. Zhao S B, Grishman R. Extracting relations with integrated information using kernel methods. In Proc. ACL 2005, Ann Arbor, USA, Jun. 25-30, 2005, pp.419-426.

  6. Zhou G D, Su J, Zhang J, Zhang M. Exploring various knowledge in relation extraction. In Proc. ACL 2005, Ann Arbor, USA, Jun. 25-30, 2005, pp.427-434.

  7. Zelenko D, Aone C, Richardella. Kernel methods for relation extraction. Journal of Machine Learning Research, 2003, 3: 1083-1106.

    MATH  MathSciNet  Google Scholar 

  8. Culotta A, Sorensen J. Dependency tree kernels for relation extraction. In Proc. ACL 2004, Barcelona, Spain, Jul. 21-26, 2004, pp.423-429.

  9. Bunescu R, Mooney R J. A shortest path dependency kernel for relation extraction. In Proc. HLT/EMNLP2005, Vancouver, Canada, Oct. 6-8, 2005, pp.724-731.

  10. Zhang M, Zhang J, Su J, Zhou G D. A composite kernel to extract relations between entities with both flat and structured features. In Proc. COLING-ACL 2006, Sydney, Australia, Jul. 17-21, 2006, pp.825-832.

  11. Collins M, Duffy N. Convolution kernels for natural language. In Proc. NIPS 2001, Vancouver, Canada, Dec. 3-8, 2001, pp.625-632.

  12. Zhou G D, Zhang M, Ji D H, Zhu Q M. Tree kernel-based relation extraction with context-sensitive structured parse tree information. In Proc. EMNLP-CoNLL 2007, Prague, Czech, Jun. 28-30, 2007, pp.728-736.

  13. Petrov S, Barrett L, Thibaux R, Klein D. Learning accurate, compact and interpretable tree annotation. In Proc. ACL 2006, Sydney, Australia, Jul. 17-21, 2006, pp.433-440.

  14. Moschitti A. A study on convolution kernels for shallow parsing. In Proc. ACL 2004, Barcelona, Spain, Jul. 21-26, 2004, pp.335-342.

  15. Zhang M, Che W X, Aw A T, Tan C L, Zhou G D, Liu T, Li S. A grammar-driven convolution tree kernel for semantic role classification. In Proc. ACL 2007, Prague, Czech, Jun. 23-30, 2007, pp.200-207.

  16. Moschitti A. Efficient convolution kernels for dependency and constituent syntactic trees. In Proc. ECML2006, Berlin, Germany, Sept. 18-22, 2006, pp.318-329.

  17. Klein D, Manning C D. Accurate unlexicalized parsing. In Proc. ACL 2003, Sapporo, Japan, Jul. 7-12, 2003, pp.423-430.

  18. Petrov S, Klein D. Discriminative log-linear grammars with latent variables. In Proc. NIPS 2008, Vancouver, Canada, Dec. 8-13, 2008, pp.1-8.

  19. Charniak E. Immediate-head parsing for language models. In Proc. ACL 2001, Tonlouse, France, Jul. 9-11, 2001, pp.129-137.

  20. Joachims T. Text categorization with support vector machine: Learning with many relevant features. In Proc. ECML1998, Chemnitz, Germany, Apr. 21-23, 1998, pp.137-142.

  21. Moschitti A. Making tree kernels practical for natural language learning. In Proc. EACL 2006, Trento, Italy, Apr. 3-7, 2006, pp.113-120.

  22. Zhang M, Che W X, Zhou G D, Aw A T, Tan C L, Liu T, Li S. Semantic role labeling using a grammar-driven convolution tree kernel. IEEE Transaction on Audio, Speech and Language Processing, 2008, 16(7): 1315-1329.

    Article  Google Scholar 

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Authors and Affiliations

  1. NLP Lab, School of Computer Science and Technology, Soochow University, Suzhou, 215006, China

    Guo-Dong Zhou (Senior Member, CCF, Member, ACM, IEEE) & Qiao-Ming Zhu (Senior Member, CCF)

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  1. Guo-Dong Zhou
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  2. Qiao-Ming Zhu
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Correspondence to Guo-Dong Zhou.

Additional information

Supported by the National Natural Science Foundation of China under Grant Nos. 60873150, 60970056 and 90920004.

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Zhou, GD., Zhu, QM. Kernel-based semantic relation detection and classification via enriched parse tree structure. J. Comput. Sci. Technol. 26, 45–56 (2011). https://doi.org/10.1007/s11390-011-9414-9

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  • DOI: https://doi.org/10.1007/s11390-011-9414-9

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