Paper: Fast and Robust Joint Models for Biomedical Event Extraction

ACL ID D11-1001
Title Fast and Robust Joint Models for Biomedical Event Extraction
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2011
Authors

Extracting biomedical events from literature has attracted much recent attention. The best- performing systems so far have been pipelines of simple subtask-specific local classifiers. A natural drawback of such approaches are cas- cading errors introduced in early stages of the pipeline. We present three joint models of increasing complexity designed to overcome this problem. The first model performs joint trigger and argument extraction, and lends it- self to a simple, efficient and exact infer- ence algorithm. The second model captures correlations between events, while the third modelensuresconsistencybetweenarguments of the same event. Inference in these models is kept tractable through dual decomposition. The first two models outperform the previous best joint approaches and are very ...