Min Zhang
Jie Zhang
ABSTRACT
This paper proposes to use a convolution kernel over parse trees to model syntactic structure information for relation extraction. Our study reveals that the syntactic structure features embedded in a parse tree are very effective for relation extraction and these features can be well captured by the convolution tree kernel. Evaluation on the ACE 2003 corpus shows that the convolution kernel over parse trees can achieve comparable performance with the previous best-reported feature-based methods on the 24 ACE relation subtypes. It also shows that our method significantly outperforms the previous two dependency tree kernels on the 5 ACE relation major types.
- ACE. 2004. The Automatic Content Extraction (ACE) Projects. http://www.ldc.upenn.edu/Projects/ACE/.Google Scholar
- Bunescu R. C. and Mooney R. J. 2005. A Shortest Path Dependency Kernel for Relation Extraction. EMNLP-2005 Google ScholarDigital Library
- Charniak E. 2001. Immediate-head Parsing for Language Models. ACL-2001 Google ScholarDigital Library
- Collins M. and Duffy N. 2001. Convolution Kernels for Natural Language. NIPS-2001Google Scholar
- Culotta A. and Sorensen J. 2004. Dependency Tree Kernel for Relation Extraction. ACL-2004 Google ScholarDigital Library
- Haussler D. 1999. Convolution Kernels on Discrete Structures. Technical Report UCS-CRL-99-10, University of California, Santa Cruz.Google Scholar
- Joachims T. 1998. Text Categorization with Support Vecor Machine: learning with many relevant features. ECML-1998 Google ScholarDigital Library
- Kambhatla Nanda. 2004. Combining lexical, syntactic and semantic features with Maximum Entropy models for extracting relations. ACL-2004 (poster) Google ScholarDigital Library
- Lodhi H., Saunders C., Shawe-Taylor J., Cristianini N. and Watkins C. 2002. Text classification using string kernel. Journal of Machine Learning Research, 2002(2):419--444 Google ScholarDigital Library
- Miller S., Fox H., Ramshaw L. and Weischedel R. 2000. A novel use of statistical parsing to extract information from text. NAACL-2000 Google ScholarDigital Library
- Moschitti Alessandro. 2004. A Study on Convolution Kernels for Shallow Semantic Parsing. ACL-2004 Google ScholarDigital Library
- MUC. 1987-1998. The nist MUC website: http://www.itl.nist.gov/iaui/894.02/related_projects/muc/.Google Scholar
- Suzuki J., Hirao T., Sasaki Y. and Maeda E. 2003. Hierarchical Directed Acyclic Graph Kernel: Methods for Structured Natural Language Data. ACL-2003 Google ScholarDigital Library
- Vishwanathan S. V. N. and Smola A. J. 2002. Fast kernels for String and Tree Matching. NIPS-2002Google Scholar
- Zelenko D., Aone C. and Richardella A. 2003. Kernel Methods for Relation Extraction. Journal of Machine Learning Research. 2003(2): 1083--1106 Google ScholarDigital Library
- Zhao Shubin and Grishman Ralph. 2005. Extracting Relations with Integrated Information Using Kernel Methods. ACL-2005 Google ScholarDigital Library
- Zhou Guodong, Su Jian, Zhang Jie and Zhang Min. 2005. Exploring Various Knowledge in Relation Extraction. ACL-2005 Google ScholarDigital Library
- Exploring syntactic features for relation extraction using a convolution tree kernel
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