Journal of Clinical and Bioanalytical Chemistry

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Clinical Bioinformatics

How to excerpt effectively biomedical events in biomedical quantity remains a big challenge for the biomedical public. As a requirement step in biomedical event extraction and event trigger identification has involved growing attention in the biomedical study. Current approaches to  the biomedical event activate identification have 2 major disadvantages each sentence in a biomedical document is separately handled, which was ignores the global situation of documents they fails to treat the issue of unfair 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 on the framework which addresses efficiently the 2 mentioned challenges accordingly. Exactly, the syntactic dependend on the tree and hierarchical care mechanism are utilized to the model both local and global contexts.

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