Search-based Structured Prediction applied to Biomedical Event Extraction
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If you have a question about this talk, please contact Thomas Lippincott.
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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