High-precision biological event extraction with a concept recognizer

Authors:
K. Bretonnel Cohen;Karin Verspoor;Helen L. Johnson;Chris Roeder;Philip V. Ogren;William A. Baumgartner, Jr.;Elizabeth White;Hannah Tipney;Lawrence Hunter
Affiliations:
University of Colorado Denver School of Medicine, Aurora, CO;University of Colorado Denver School of Medicine, Aurora, CO;University of Colorado Denver School of Medicine, Aurora, CO;University of Colorado Denver School of Medicine, Aurora, CO;University of Colorado Denver School of Medicine, Aurora, CO;University of Colorado Denver School of Medicine, Aurora, CO;University of Colorado Denver School of Medicine, Aurora, CO;University of Colorado Denver School of Medicine, Aurora, CO;University of Colorado Denver School of Medicine, Aurora, CO
Venue:
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task
Year:
2009

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Abstract

We approached the problems of event detection, argument identification, and negation and speculation detection as one of concept recognition and analysis. Our methodology involved using the OpenDMAP semantic parser with manually-written rules. We achieved state-of-the-art precision for two of the three tasks, scoring the highest of 24 teams at precision of 71.81 on Task 1 and the highest of 6 teams at precision of 70.97 on Task 2. The OpenDMAP system and the rule set are available at bionlp.sourceforge.net.