Exploiting Rich Syntactic Information for Relation Extraction from Biomedical Articles∗

This paper proposes a ternary relation extraction method primarily based on rich syntactic information. We identify PROTEIN-ORGANISM-LOCATION relations in the text of biomedical articles. Different kernel functions are used with an SVM learner to integrate two sources of information from syntactic parse trees: (i) a large number of syntactic features that have been shown useful for Semantic Role Labeling (SRL) and applied here to the relation extraction task, and (ii) features from the entire parse tree using a tree kernel. Our experiments show that the use of rich syntactic features significantly outperforms shallow word-based features. The best accuracy is obtained by combining SRL features with tree kernels.