Biomedical event extraction without training data

Authors:
Andreas Vlachos;Paula Buttery;Diarmuid Ó Séaghdha;Ted Briscoe
Affiliations:
University of Cambridge, Cambridge, UK;University of Cambridge, Cambridge, UK;University of Cambridge, Cambridge, UK;University of Cambridge, Cambridge, UK
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 describe our system for the BioNLP 2009 event detection task. It is designed to be as domain-independent and unsupervised as possible. Nevertheless, the precisions achieved for single theme event classes range from 75% to 92%, while maintaining reasonable recall. The overall F-scores achieved were 36.44% and 30.80% on the development and the test sets respectively.