The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
Automatic labeling of semantic roles
Computational Linguistics
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
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
Biomedical event annotation with CRFs and precision grammars
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task
Complex event extraction at PubMed scale
Bioinformatics
Towards event extraction from full texts on infectious diseases
BioNLP '10 Proceedings of the 2010 Workshop on Biomedical Natural Language Processing
Overview of BioNLP Shared Task 2011
BioNLP Shared Task '11 Proceedings of the BioNLP Shared Task 2011 Workshop
Overview of the infectious diseases (ID) task of BioNLP Shared Task 2011
BioNLP Shared Task '11 Proceedings of the BioNLP Shared Task 2011 Workshop
BioNLP Shared Task 2011: supporting resources
BioNLP Shared Task '11 Proceedings of the BioNLP Shared Task 2011 Workshop
Overview of the infectious diseases (ID) task of BioNLP Shared Task 2011
BioNLP Shared Task '11 Proceedings of the BioNLP Shared Task 2011 Workshop
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Building on technical advances from the BioNLP 2009 Shared Task Challenge, the 2011 challenge sets forth to generalize techniques to other complex biological event extraction tasks. In this paper, we present the implementation and evaluation of a signature-based machine-learning technique to predict events from full texts of infectious disease documents. Specifically, our approach uses novel signatures composed of traditional linguistic features and semantic knowledge to predict event triggers and their candidate arguments. Using a leave-one out analysis, we report the contribution of linguistic and shallow semantic features in the trigger prediction and candidate argument extraction. Lastly, we examine evaluations and posit causes for errors in our complex biological event extraction.