A memory-based learning approach to event extraction in biomedical texts

Abstract

In this paper we describe the memory-based machine learning system that we submitted to the BioNLP Shared Task on Event Extraction. We modeled the event extraction task using an approach that has been previously applied to other natural language processing tasks like semantic role labeling or negation scope finding. The results obtained by our system (30.58 F-score in Task 1 and 29.27 in Task 2) suggest that the approach and the system need further adaptation to the complexity involved in extracting biomedical events.