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Indexed by:会议论文

Date of Publication:2016-01-01

Included Journals:CPCI-S

Page Number:775-775

Key Words:Biomedical event extraction; Distributed representation; Deep learning; Convolutional neural network

Abstract:The two main problems of biomedical event extraction are trigger identification and argument detection which can both be considered as classification problems. In this paper, we propose a distributed representation method, which combines context, consisted by dependency-based word embedding, and task-based features represented in a distributed way on deep learning models to realize biomedical event extraction. The experimental results on Multi-Level Event Extraction (MLEE) corpus show higher F-scores compared to the state-of-the-art SVM method. This demonstrates that our proposed method is effective for biomedical event extraction.