A Feature-Enriched Tree Kernel for Relation Extraction

Le Sun, Xianpei Han

Abstract: Tree kernel is an effective technique for relation extraction. However, the traditional syntactic tree representation is often too coarse or ambiguous to accurately capture the semantic relation information between two entities. In this paper, we propose a new tree kernel, called feature-enriched tree kernel (FTK), which can enhance the traditional tree kernel by: 1) refining the syntactic tree representation by annotating each tree node with a set of discriminant features; and 2) proposing a new tree kernel which can better measure the syntactic tree similarity by taking all features into consideration. Experimental results show that our method can achieve a 5.4% F-measure improvement over the traditional convolution tree kernel.