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Exploring Useful Features for Biomedical Event Trigger Detection
Jian Wang, Qian Xu, Hongfei Lin, Zhihao Yang and Yanpeng Li

Event extraction has a broad range of application in systems biology, ranging from support for the creation and annotation of pathways to automatic population or enrichment of databases. In this task, trigger detection, in which we assign the event type to each token, plays a critical role. However, word sense ambiguity makes the trigger detection challenging. In this paper, we explore some new features to solve this problem. Trigger detection is addressed with a multi-class SVM classifier that assigns event classes to individual tokens. Furthermore, we have reviewed current features that have been proposed to analyze the effect of each feature. Compared with previous approach, the system achieved an F-score of 66.3% on the trigger detection in BioNLP 2011 shared task corpus.

Keywords: Event extraction, Trigger detection, Features, Word sense disambiguation, Multi-class, BioNLP

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