Postdoc in the department of Biostatistics and Medical Informatics at the University of Wisconsin-Madison

Search-based Structured Prediction applied to Biomedical Event Extraction

We develop an approach to biomedical event extraction under the search-based structured prediction framework (SEARN) which converts the task into cost-sensitive classification (CSC) 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.