Making large-scale support vector machine learning practical
Advances in kernel methods
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
Semi-supervised Prediction of Protein Interaction Sentences Exploiting Semantically Encoded Metrics
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
Classification of Protein Interaction Sentences via Gaussian Processes
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
Clairlib: a toolkit for natural language processing, information retrieval, and network analysis
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Systems Demonstrations
Biomedical events extraction using the hidden vector state model
Artificial Intelligence in Medicine
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We introduce a supervised approach for extracting bio-molecular events by using linguistic features that represent the contexts of the candidate event triggers and participants. We use Support Vector Machines as our learning algorithm and train separate models for event types that are described with a single theme participant, multiple theme participants, or a theme and a cause participant. We perform experiments with linear kernel and edit-distance based kernel and report our results on the BioNLP'09 Shared Task test data set.