期刊论文

Zhao, Weizhong;Yang, Jincai

英文

INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS

1748-5673

2020

23

3

189-212

10.1504/IJDMB.2020.107876

The work is partially supported by the National Key Research and Development Program of China (2017YFC0909502), the National Natural Science Foundation of China (61532008 and 61872157), the Wuhan Science and Technology Program (2019010701011392), the Key Research Program of Central China Normal University (CCNU18JCXK05), the Fundamental Research Funds for the Central Universities (CCNU19TD004), the Guangxi Key Laboratory of Trusted Software (No. kx201905), and the Research Fund of Guangxi Key Lab of Multi-source Information Mining & Security (MIMS19-02). The views presented in this paper are those of the authors and do not necessarily represent those of the US Food and Drug Administration.

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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...