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ACL
2006

A Composite Kernel to Extract Relations between Entities with Both Flat and Structured Features

9 years 5 months ago
A Composite Kernel to Extract Relations between Entities with Both Flat and Structured Features
This paper proposes a novel composite kernel for relation extraction. The composite kernel consists of two individual kernels: an entity kernel that allows for entity-related features and a convolution parse tree kernel that models syntactic information of relation examples. The motivation of our method is to fully utilize the nice properties of kernel methods to explore diverse knowledge for relation extraction. Our study illustrates that the composite kernel can effectively capture both flat and structured features without the need for extensive feature engineering, and can also easily scale to include more features. Evaluation on the ACE corpus shows that our method outperforms the previous best-reported methods and significantly outperforms previous two dependency tree kernels for relation extraction.
Min Zhang, Jie Zhang, Jian Su, Guodong Zhou
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2006
Where ACL
Authors Min Zhang, Jie Zhang, Jian Su, Guodong Zhou
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