Convolution kernels on constituent, dependency and sequential structures for relation extraction

EMNLP 2009 - Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: A Meeting of SIGDAT, a Special Interest Group of ACL, Held in Conjunction with ACL-IJCNLP 2009

Volumn , Issue , 2009, Pages 1378-1387

  Nguyen, Truc Vien T ^a   Moschitti, Alessandro ^a   Riccardi, Giuseppe ^a  

^a UNIVERSITY OF TRENTO   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

EXTRACTION; FORESTRY; SEMANTICS; SUPPORT VECTOR MACHINES; SYNTACTICS; TREES (MATHEMATICS);

CONVOLUTION KERNEL; ENTITY-TYPES; PARTIAL TREES; RELATION EXTRACTION; SEMANTIC STRUCTURES; SEQUENTIAL STRUCTURE; STATE OF THE ART; SYNTACTIC TREES;

NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 80053401742     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3115/1699648.1699684     Document Type: Conference Paper

References (39)

1. Eugene Agichtein and Luis Gravano. 2000. Snowball: Extracting relations from large plain-text collections. In Proceedings of the 5th ACM International Conference on Digital Libraries.
2. Roberto Basili, Marco Cammisa, and Alessandro Moschitti. 2005a. Effective use of WordNet semantics via kernel-based learning. In Proceedings of the Ninth Conference on Computational Natural Language Learning (CoNLL-2005), pages 1-8, Ann Arbor, Michigan, June. Association for Computational Linguistics.
3. Roberto Basili, Marco Cammisa, and Alessandro Moschitti. 2005b. A semantic kernel to classify texts with very few training examples. In In Proceedings of the Workshop on Learning in Web Search, at the.
4. Stephan Bloehdorn and Alessandro Moschitti. 2007. Structure and semantics for expressive text kernels. In CIKM '07: Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, pages 861-864, New York, NY, USA. ACM.
5. Stephan Bloehdorn, Roberto Basili, Marco Cammisa, and Alessandro Moschitti. 2006. Semantic kernels for text classification based on topological measures of feature similarity. In Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 06), Hong Kong, 18-22 December 2006, DEC.
6. Sergey Brin. 1998. Extracting patterns and relations from world wide web. In Proceeding of WebDB Workshop at 6th International Conference on Extending Database Technology, pages 172-183.
7. Razvan C. Bunescu and Raymond J. Mooney. 2005a. A shortest path dependency kernel for relation extraction. In Proceedings of EMNLP, pages 724-731.
8. Razvan C. Bunescu and Raymond J. Mooney. 2005b. Subsequence kernels for relation extraction. In Proceedings of EMNLP.
9. Nicola Cancedda, Eric Gaussier, Cyril Goutte, and Jean Michel Renders. 2003. Word sequence kernels. Journal of Machine Learning Research, pages 1059-1082.
10. Michael Collins and Nigel Duffy. 2001. Convolution kernels for natural language. In Proceedings of Neural Information Processing Systems (NIPS'2001).
11. Aron Culotta and Jeffrey Sorensen. 2004. Dependency tree kernels for relation extraction. In Proceedings of the 42nd Annual Meeting on ACL, Barcelona, Spain.
12. Ana-Maria Giuglea and Alessandro Moschitti. 2004. Knowledge discovery using framenet, verbnet and propbank. In A. Meyers, editor, Workshop on Ontology and Knowledge Discovering at ECML 2004, Pisa, Italy.
13. Ana-Maria Giuglea and Alessandro Moschitti. 2006. Semantic Role Labeling via Framenet, Verbnet and Propbank. In Proceedings of ACL 2006, Sydney, Australia.
14. Takaaki Hasegawa, Satoshi Sekine, and Ralph Grishman. 2004. Discovering relations among named entities from large corpora. In Proceedings of the 42nd Annual Meeting on ACL, Barcelona, Spain.
15. Nanda Kambhatla. 2004. Combining lexical, syntactic and semantic features with maximum entropy models for extracting relations. In Proceedings of the ACL 2004 on Interactive poster and demonstration sessions, Barcelona, Spain.
16. Dan Klein and Christopher D. Manning. 2003. Accurate unlexicalized parsing. In Proceedings of the 41st Meeting of the ACL, pages 423-430.
17. Huma Lodhi, Craig Saunders, John Shawe-Taylor, Nello Cristianini, and Chris Watkins. 2002. Text classification using string kernels. Journal of Machine Learning Research, pages 419-444.
18. Mitchell P. Marcus, Beatrice Santorini, and Mary Ann Marcinkiewicz. 1993. Building a large annotated corpus of english: the penn treebank. Computational Linguistics, 19(2):313-330.
19. Scott Miller, Heidi Fox, Lance Ramshaw, and Ralph Weischedel. 2000. A novel use of statistical parsing to extract information from text. In Proceedings of the 1st conference on North American chapter of the ACL, pages 226-233, Seattle, USA.
20. Alessandro Moschitti and Cosmin Bejan. 2004. A semantic kernel for predicate argument classification. In CoNLL-2004, Boston, MA, USA.
21. Alessandro Moschitti, Silvia Quarteroni, Roberto Basili, and Suresh Manandhar. 2007. Exploiting syntactic and shallow semantic kernels for question/answer classification. In Proceedings of ACL'07, Prague, Czech Republic.
22. Alessandro Moschitti, Daniele Pighin, and Roberto Basili. 2008. Tree kernels for semantic role labeling. Computational Linguistics, 34(2):193-224.
23. Alessandro Moschitti. 2004. A study on convolution kernels for shallow semantic parsing. In Proceedings of the 42nd Meeting of the ACL, Barcelona, Spain.
24. Alessandro Moschitti. 2006. Efficient convolution kernels for dependency and constituent syntactic trees. In Proceedings of the 17th European Conference on Machine Learning, Berlin, Germany.
25. Alessandro Moschitti. 2008. Kernel methods, syntax and semantics for relational text categorization. In CIKM '08: Proceeding of the 17th ACM conference on Information and knowledge management, pages 253-262, New York, NY, USA. ACM.
26. Ryan Michael Rifkin and Tomaso Poggio. 2002. Everything old is new again: a fresh look at historical approaches in machine learning. PhD thesis, Massachusetts Institute of Technology.
27. Klaus robert Mller, Sebastian Mika, Gunnar Rtsch, Koji Tsuda, and Bernhard Schlkopf. 2001. An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks, 12(2):181-201.
28. Dan Roth and Wen tau Yih. 2002. Probabilistic reasoning for entity and relation recognition. In Proceedings of the COLING-2002, Taipei, Taiwan.
29. Bernhard Schlkopf and Alexander J. Smola. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge, MA, USA.
30. Vladimir N. Vapnik. 1995. The Nature of Statistical Learning Theory. Springer-Verlag, New York.
31. Vladimir N. Vapnik. 1998. Statistical Learning Theory. John Wiley and Sons, New York.
32. S.V.N. Vishwanathan and Alexander J. Smola. 2002. Fast kernels on strings and trees. In Proceedings of Neural Information Processing Systems.
33. Mengqiu Wang. 2008. A re-examination of dependency path kernels for relation extraction. In Proceedings of the 3rd International Joint Conference on Natural Language Processing-IJCNLP.
34. Dmitry Zelenko, Chinatsu Aone, and Anthony Richardella. 2002. Kernel methods for relation extraction. In Proceedings of EMNLP-ACL, pages 181-201.
35. Min Zhang, Jian Su, Danmei Wang, Guodong Zhou, and Chew Lim Tan. 2005. Discovering relations between named entities from a large raw corpus using tree similarity-based clustering. In Proceedings of IJCNLP'2005, Lecture Notes in Computer Science (LNCS 3651), pages 378-389, Jeju Island, South Korea.
36. Min Zhang, Jie Zhang, Jian Su, and Guodong Zhou. 2006. A composite kernel to extract relations between entities with both flat and structured features. In Proceedings of COLING-ACL 2006, pages 825-832.
37. Shubin Zhao and Ralph Grishman. 2005. Extracting relations with integrated information using kernel methods. In Proceedings of the 43rd Meeting of the ACL, pages 419-426, Ann Arbor, Michigan, USA.
38. GuoDong Zhou, Jian Su, Jie Zhang, and Min Zhang. 2005. Exploring various knowledge in relation extraction. In Proceedings of the 43rd Meeting of the ACL, pages 427-434, Ann Arbor, USA, June.
39. GuoDong Zhou, Min Zhang, DongHong Ji, and QiaoMing Zhu. 2007. Tree kernel-based relation extraction with context-sensitive structured parse tree information. In Proceedings of EMNLP-CoNLL 2007, pages 728-736.