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Search-based Structured Prediction applied to Biomedical Event Extraction

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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. Additionally, we consider the issue of cost estimation during learning and present an approach called focused costing that improves improves efficiency and predictive accuracy.

This talk is part of the NLIP Seminar Series series.

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