Semantic classification of bio-entities incorporating predicate-argument features

In this paper, we propose new external context features for the semantic classification of bio-entities. In the previous approaches, the words located on the left or the right context of bio-entities are frequently used as the external context features. However, in our prior experiments, the external contexts in a flat representation did not improve the performance. In this study, we incorporate predicate-argument features into training the ME-based classifier. Through parsing and argument identification, we recognize biomedical verbs that have argument relations with the constituents including a bio-entity, and then use the predicate-argument structures as the external context features. The extraction of predicateargument features can be done by performing two identification tasks: the biomedically salient word identification which determines whether a word is a biomedically salient word or not, and the target verb identification which identifies biomedical verbs that have argument relations with the constituents including a bio-entity. Experiments show that the performance of semantic classification in the bio domain can be improved by utilizing such predicate-argument features.