2011b. Event extraction as dependency parsing for BioNLP 2011

We describe the Stanford entry to the BioNLP 2011 shared task on biomolecular event ex-traction (Kim et al., 2011a). Our framework is based on the observation that event structures bear a close relation to dependency graphs. We show that if biomolecular events are cast as these pseudosyntactic structures, standard parsing tools (maximum-spanning tree parsers and parse rerankers) can be applied to per-form event extraction with minimum domain-specific tuning. The vast majority of our domain-specific knowledge comes from the conversion to and from dependency graphs. Our system performed competitively, obtain-ing 3rd place in the Infectious Diseases track (50.6 % f-score), 5th place in Epigenetics and Post-translational Modifications (31.2%), and 7th place in Genia (50.0%). Additionally, this system was part of the combined system in Riedel et al. (2011) to produce the highest scoring system in three out of the four event extraction tasks.