Unsupervised event extraction from biomedical literature using co-occurrence information and basic patterns

In this paper, we propose a new unsupervised method of extracting events from biomedical literature, which uses the score measures of events and patterns having reciprocal effects on each other. We, first, generate candidate events by performing linguistic preprocessing and utilizing basic event pattern information, and then extract reliable events based on the event score which is estimated by using co-occurrence information of candidate event's arguments and pattern score. Unlike the previous approaches, the proposed approach does not require a huge number of rules and manually constructed training corpora. Experimental results on GENIA corpora show that the proposed method can achieve high recall (69.7%) as well as high precision (90.3%).