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Event Extraction as Dependency Parsing for BioNLP 2011
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@MISC{Mcclosky_eventextraction,
author = {David Mcclosky and Mihai Surdeanu and Christopher D. Manning},
title = {Event Extraction as Dependency Parsing for BioNLP 2011},
year = {}
}
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Abstract
We describe the Stanford entry to the BioNLP 2011 shared task on biomolecular event extraction (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 perform event extraction with minimum domainspecific tuning. The vast majority of our domain-specific knowledge comes from the conversion to and from dependency graphs. Our system performed competitively, obtaining 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. 1
Keyphrases
event extraction dependency parsing domain-specific knowledge event extraction task scoring system standard parsing tool minimum domainspecific tuning vast majority dependency graph maximum-spanning tree parser parse rerankers infectious disease track pseudosyntactic structure stanford entry close relation 5th place post-translational modification combined system biomolecular event extraction biomolecular event
