Biomedical event detection using rules, conditional random fields and parse tree distances

Authors:
Farzaneh Sarafraz;James Eales;Reza Mohammadi;Jonathan Dickerson;David Robertson;Goran Nenadic
Affiliations:
University of Manchester;University of Manchester;Sharif University of Technology;University of Manchester;University of Manchester;University of Manchester
Venue:
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task
Year:
2009

Citing 2
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Abstract

This paper reports on a system developed for the BioNLP'09 shared task on detection and characterisation of biomedical events. Event triggers and types were recognised using a conditional random field classifier and a set of rules, while event participants were identified using a rule-based system that relied on relative distances between candidate entities and the trigger in the associated parse tree. The results on previously unseen test data were encouraging: for non-regulatory events, the F-score was almost 50% (with precision above 60%), with the overall F-score of around 30% (49% precision). The performance on more complex regulatory events was poor (F-measure of 7%). Among the 24 teams submitting the test results, our results were ranked 12th for the overall F-score and 8th for the F-score of non-regulation events.