Search-based structured prediction applied to biomedical event extraction

We develop an approach to biomedical event extraction using a search-based structured pre-diction framework, SEARN, which converts the task into cost-sensitive classification tasks whose models are learned jointly. We show that SEARN improves on a simple yet strong pipeline by 8.6 points in F-score on the BioNLP 2009 shared task, while achieving the best reported performance by a joint inference method. Additionally, we consider the issue of cost estimation during learning and present an approach called focused costing that improves improves efficiency and predictive accuracy.