A feature-enriched tree kernel for relation extraction

52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conference

Volumn 2, Issue , 2014, Pages 61-67

  Sun, Le ^a   Han, Xianpei ^a  

^a INSTITUTE OF SOFTWARE   (China)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS; EXTRACTION; SEMANTICS; SYNTACTICS;

F MEASURE; RELATION EXTRACTION; SEMANTIC RELATIONS; SYNTACTIC TREES; TREE KERNELS; TREE NODES;

FORESTRY;

EXTRACTION; FORESTRY; TREES;

EID: 84906931876     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3115/v1/p14-2011     Document Type: Conference Paper

References (34)

1. Agichtein, E. and Gravano, L. 2000. Snowball: Extracting relations from large plain-text collections. In: Proceedings of the 5th ACM Conference on Digital Libraries, pp. 85-94.
2. Plank, B. and Moschitti, A. 2013. Embedding Semantic Similarity in Tree Kernels for Domain Adaptation of Relation Extraction". In: Proceedings of ACL 2013.
3. Banko, M., Cafarella, M. J., Soderland, S., Broadhead, M. and Etzioni, O. 2007. Open information extraction from the Web. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence, pp. 2670-2676.
4. Bunescu, R. and Mooney, R. 2005. A shortest path dependency kernel for relation extraction. In: Proceedings of the Human Language Technology Conference and the Conference on Empirical Methods in Natural Language Processing, pp.724-731.
5. Bloehdorn, S. and Moschitti, A. 2007a. Combined Syntactic and Semantic Kernels for Text Classification. In: Proceedings of the 29th European Conference on Information Retrieval (ECIR).
6. Bloehdorn, S. and Moschitti, A. 2007b. Structure and semantics for expressive text kernels. In: Proceeding of ACM 16th Conference on Information and Knowledge Management (CIKM).
7. Bunescu, R. and Mooney. R., 2006. Subsequence kernels for relation extraction. In: Advances in Neural Information Processing Systems 18, pp. 171-178.
8. Charniak, E., 2001. Immediate-head parsing for language models. In: Proceedings of the 39th Annual Meeting on Association for Computational Linguistics, pp. 124-131.
9. Chan, Y. S. and Roth, D. 2010. Exploiting background knowledge for relation extraction. In: Proceedings of the 23rd International Conference on Computational Linguistics, pp. 152-160.
10. Chan, Y S. and Roth, D. 2011. Exploiting syntacticosemantic structures for relation extraction. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pp. 551-560.
11. Croce, D., Moschitti, A. and Basili, R. 2011. Structured lexical similarity via convolution kernels on dependency trees. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pp. 1034-1046.
12. Croce, D., Moschitti, A., Basili, R. and Palmer, M. 2012. Verb Classification using Distributional Similarity in Syntactic and Semantic Structures. In: Proceedings of ACL 2012, pp. 263-272.
13. Culotta, A. and Sorensen, J. 2004. Dependency tree kernels for relation extraction. In: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics, pp. 423-429.
14. Grishman, R. and Sundheim, B. 1996. Message understanding conference-6: A brief history. In: Proceedings of the 16th International Conference on Computational Linguistics, pp. 466-471.
15. Collins, M. and Duffy, N., 2001. Convolution Kernels for Natural Language. In: Proceedings of NIPS 2001.
16. Liu, D., et al. 2013. Incorporating lexical semantic similarity to tree kernel-based Chinese relation extraction. In: Proceedings of Chinese Lexical Semantics 2013.
17. Jiang, J. and Zhai, C. 2007. A systematic exploration of the feature space for relation extraction. In: Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, pp. 113-120.
18. Joachims, T. 1998. Text Categorization with Su pport Vector Machine: learning with many relevant features. ECML-1998: 137-142.
19. Kambhatla, N. 2004. Combining lexical, syntactic, and semantic features with maximum entropy models for extracting relations. In: the Proceedings of 42st Annual Meeting of the Association for Computational Linguistics, pp. 178-181.
20. Krause, S., Li, H., Uszkoreit, H., & Xu, F. 2012. Largescale learning of relation-extraction rules with distant supervision from the web. In: Proceedings of ISWC 2012, pp. 263-278.
21. Marcus, M. P., Marcinkiewicz, M. A., & Santorini, B. 1993. Building a large annotated corpus of English: The Penn Treebank. Computational linguistics, 19(2), 313-330.
22. Moschitti, A. 2004. A study on Convolution Kernels for Shallow Semantic Parsing. In: Proceedings of the 42-th Conference on Association for Computational Linguistic (ACL-2004).
23. Moschitti, A. 2009. Syntactic and semantic kernels for short text pair categorization. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 576-584.
24. Mehdad, Y., Moschitti, A. and Zanzotto, F. 2010. Syntactic/ Semantic Structures for Textual Entailment Recognition. In: Proceedings of Human Language Technology-North American chapter of the Association for Computational Linguistics.
25. Mintz, M., Bills, S., Snow, R. and Jurafsky D. 2009. Distant supervision for relation extraction without labeled data. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the Association for Computational Linguistics and the 4th International Joint Conference on Natural Language Processing of the AFNLP, pp. 1003-1011.
26. Qian L., Zhou G., Kong F., Zhu Q., and Qian P., 2008. Exploiting constituent dependencies for tree kernel based semantic relation extraction. In: Proceedings of the 22nd International Conference on Computational Linguistics, pp. 697-704.
27. Reichartz, F. and H. Korte, et al. 2010. Semantic relation extraction with kernels over typed dependency trees. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining.
28. Zelenko, D., Aone, C., and Richardella, A. 2003. Kernel methods for relation extraction. Journal of Machine Learning Research, 3:1083-1106.
29. Zhang, M., Zhang, J., and Su, J. 2006. Exploring syntactic features for relation extraction using a convolution tree kernel. In: Proceedings of the Human Language Technology Conference and the North American Chapter of the Association for Computational Linguistics, pages 288-295.
30. Zhang, M., Zhang, J., Su, J. and Zhou, G. 2006. 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 the 44th Annual Meeting of the Association for Computational Linguistics, pages 825-832.
31. Zhao, S. and Grishman, R. 2005. Extracting relations with integrated information using kernel methods. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics, pages 419-426.
32. Zhou, G., Su, J., Zhang, J., and Zhang, M. 2005. Exploring various knowledge in relation extraction. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics, pages 427-434.
33. Zhou, G. and Zhang M. 2007. Extracting relation information from text documents by exploring various types of knowledge. Information Processing & Management 43(4): 969-982.
34. Zhou, G., et al. 2007. Tree kernel-based relation extraction with context-sensitive structured parse tree information. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 728-736.