title: Biomedical event extraction from abstracts and full papers using search-based structured prediction.
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.
authors: Computer Laboratory, University of Cambridge, UK. andreas.vlachos@cl.cam.ac.uk
authors: Andreas
authors: A
authors: Vlachos
authors: Mark
authors: M
authors: Craven
mesh_terms: D000465:Algorithms; D016247:Information Storage and Retrieval / Q000191:economics / Q000379:methods; D009323:Natural Language Processing; D010506:Periodicals as Topic