Exploring syntactic structured features over parse trees for relation extraction using kernel methods

Extracting semantic relationships between entities from text documents is challenging in information extraction and important for deep information processing and management. This paper proposes to use the convolution kernel over parse trees together with support vector machines to model syntactic structured information for relation extraction. Compared with linear kernels, tree kernels can effectively explore implicitly huge syntactic structured features embedded in a parse tree. Our study reveals that the syntactic structured features embedded in a parse tree are very effective in relation extraction and can be well captured by the convolution tree kernel. Evaluation on the ACE benchmark corpora shows that using the convolution tree kernel only can achieve comparable performance with previous best-reported feature-based methods. It also shows that our method significantly outperforms previous two dependency tree kernels for relation extraction. Moreover, this paper proposes a composi...

• Type: Journal
• Year: 2008
• Where: IPM
• Authors: Min Zhang, Guodong Zhou, AiTi Aw
• Keywords: Convolution Tree Kernel, IPM 2008, Parse Tree, Relation Extraction