Paper: A Composite Kernel To Extract Relations Between Entities With Both Flat And Structured Features

ACL ID P06-1104
Title A Composite Kernel To Extract Relations Between Entities With Both Flat And Structured Features
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2006
Authors

This paper proposes a novel composite ker- nel 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 fea- tures. Evaluation on the ACE corpus shows that our method outperforms the previous best-reported methods and significantly out- performs previous two dependency tree ker- nels ...