Tunable domain-independent event extraction in the MIRA framework

Authors:
Georgi Georgiev;Kuzman Ganchev;Vassil Momtchev;Deyan Peychev;Preslav Nakov;Angus Roberts
Affiliations:
Ontotext AD, Sofia, Bulgaria;Ontotext AD, Sofia, Bulgaria;Ontotext AD, Sofia, Bulgaria;Ontotext AD, Sofia, Bulgaria;Ontotext AD, Sofia, Bulgaria;Sheffield, 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 the system of the PIKB team for BioNLP'09 Shared Task 1, which targets tunable domain-independent event extraction. Our approach is based on a three-stage classification: (1) trigger word tagging, (2) simple event extraction, and (3) complex event extraction. We use the MIRA framework for all three stages, which allows us to trade precision for increased recall by appropriately changing the loss function during training. We report results for three systems focusing on recall (R = 28.88%), precision (P = 65.58%), and F1-measure (F1 = 33.57%), respectively.