Generalizing an Approximate Subgraph Matching-based System to Extract Events in Molecular Biology and Cancer Genetics

We participated in the BioNLP 2013 shared tasks, addressing the GENIA (GE) and the Cancer Genetics (CG) event extraction tasks. Our event extraction is based on the system we recently proposed for mining relations and events involving genes or proteins in the biomedical literature using a novel, approximate subgraph matching-based approach. In addition to handling the GE task involving 13 event types uniformly related to molecular biology, we generalized our system to address the CG task targeting a challenging set of 40 event types related to cancer biology with various arguments involving 18 kinds of biological entities. Moreover, we attempted to integrate a distributional similarity model into our system to extend the graph matching scheme for more events. In addition, we evaluated the impact of using paths of all possible lengths among event participants as key contextual dependencies to extract potential events as compared to using only the shortest paths within the framework of our system. We achieved a 46.38 % F-score in the CG task and a 48.93 % F-score in the GE task, ranking 3rd and 4th respectively. The consistent performance confirms that our system generalizes well to various event extraction tasks and scales to handle a large number of event and entity types.