As a prerequisite step in biomedical event extraction, event trigger identification has attracted growing attention in biomedical research. Existing approaches to biomedical event trigger identification have two major drawbacks: (1) each sentence in a biomedical document is handled separately, which ignores the global context; (2) they fail to treat the issue of imbalanced class which is induced by the sparseness of event triggers in biomedical documents. To improve the performance of biomedical event trigger identification, we propose a deep neural network-based framework which addresses effe...
