MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic labeling of semantic roles
Computational Linguistics
Applied morphological processing of English
Natural Language Engineering
Online Passive-Aggressive Algorithms
The Journal of Machine Learning Research
Search-based structured prediction
Machine Learning
Self-training for biomedical parsing
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Overview of BioNLP'09 shared task on event extraction
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task
Extracting complex biological events with rich graph-based feature sets
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task
A Markov logic approach to bio-molecular event extraction
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task
Joint inference for knowledge extraction from biomedical literature
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Evaluating dependency representation for event extraction
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Event extraction as dependency parsing
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Biomedical event extraction from abstracts and full papers using search-based structured prediction
BioNLP Shared Task '11 Proceedings of the BioNLP Shared Task 2011 Workshop
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We develop an approach to biomedical event extraction using a search-based structured prediction 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.