Learning to Extract Biological Event and Relation Graphs

While the overwhelming majority of information extraction efforts in the biomedical domain have focused on the extraction of simple binary interactions between named entity pairs, some recently published corpora provide complex, nested and typed event annotations that aim to accurately capture the diversity of biological relationships. We present the first machine learning approach for extracting such relationships, utilizing both a graph kernel and a novel, task-specific feature set. We show that relationships can be predicted with 77% F-score, or 83% if their type and direction is disregarded. Using both gold standard and generated parses, we determine the impact of parsing on extraction performance. Finally, we convert our predicted complex relationships to binary interactions, recovering binary annotation with 62% F-score, relating the new method to the large body of work available on binary interactions.