A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Optimizing syntax patterns for discovering protein-protein interactions
Proceedings of the 2005 ACM symposium on Applied computing
RelEx---Relation extraction using dependency parse trees
Bioinformatics
A graph kernel for protein-protein interaction extraction
BioNLP '08 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
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
Integration of static relations to enhance event extraction from text
BioNLP '10 Proceedings of the 2010 Workshop on Biomedical Natural Language Processing
The CoNLL-2010 shared task: learning to detect hedges and their scope in natural language text
CoNLL '10: Shared Task Proceedings of the Fourteenth Conference on Computational Natural Language Learning --- Shared Task
A parser-based approach to detecting modification of biomedical events
Proceedings of the ACM fifth international workshop on Data and text mining in biomedical informatics
Generalizing biomedical event extraction
BioNLP Shared Task '11 Proceedings of the BioNLP Shared Task 2011 Workshop
Cross-genre and cross-domain detection of semantic uncertainty
Computational Linguistics
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The BioNLP'09 Shared Task on Event Extraction is a challenge which concerns the detection of bio-molecular events from text. In this paper, we present a detailed account of the challenges encountered during the construction of a machine learning framework for participation in this task. We have focused our work mainly around the filtering of false positives, creating a high-precision extraction method. We have tested techniques such as SVMs, feature selection and various filters for data pre- and post-processing, and report on the influence on performance for each of them. To detect negation and speculation in text, we describe a custom-made rule-based system which is simple in design, but effective in performance.