Paper: Exploring Syntactic Features For Relation Extraction Using A Convolution Tree Kernel

ACL ID N06-1037
Title Exploring Syntactic Features For Relation Extraction Using A Convolution Tree Kernel
Venue Human Language Technologies
Session Main Conference
Year 2006
Authors

This paper proposes to use a convolution kernel over parse trees to model syntactic structure information for relation extrac- tion. Our study reveals that the syntactic structure features embedded in a parse tree are very effective for relation extrac- tion and these features can be well cap- tured by the convolution tree kernel. Evaluation on the ACE 2003 corpus shows that the convolution kernel over parse trees can achieve comparable per- formance with the previous best-reported feature-based methods on the 24 ACE re- lation subtypes. It also shows that our method significantly outperforms the pre- vious two dependency tree kernels on the 5 ACE relation major types.