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This article is part of the supplement: Selected articles from the BioNLP Shared Task 2011

Open Access Proceedings

Biomedical event extraction from abstracts and full papers using search-based structured prediction

Andreas Vlachos1* and Mark Craven2

Author Affiliations

1 Computer Laboratory, University of Cambridge, UK

2 Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, USA

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BMC Bioinformatics 2012, 13(Suppl 11):S5  doi:10.1186/1471-2105-13-S11-S5

Published: 26 June 2012

Abstract

Background

Biomedical event extraction has attracted substantial attention as it can assist researchers in understanding the plethora of interactions among genes that are described in publications in molecular biology. While most recent work has focused on abstracts, the BioNLP 2011 shared task evaluated the submitted systems on both abstracts and full papers. In this article, we describe our submission to the shared task which decomposes event extraction into a set of classification tasks that can be learned either independently or jointly using the search-based structured prediction framework. Our intention is to explore how these two learning paradigms compare in the context of the shared task.

Results

We report that models learned using search-based structured prediction exceed the accuracy of independently learned classifiers by 8.3 points in F-score, with the gains being more pronounced on the more complex Regulation events (13.23 points). Furthermore, we show how the trade-off between recall and precision can be adjusted in both learning paradigms and that search-based structured prediction achieves better recall at all precision points. Finally, we report on experiments with a simple domain-adaptation method, resulting in the second-best performance achieved by a single system.

Conclusions

We demonstrate that joint inference using the search-based structured prediction framework can achieve better performance than independently learned classifiers, thus demonstrating the potential of this learning paradigm for event extraction and other similarly complex information-extraction tasks.