高级工程师
性别：男
毕业院校：大连理工大学
学位：博士
所在单位：计算机科学与技术学院
学科：计算机应用技术
办公地点：创新园大厦D0103房间
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发布时间：2019-03-10
论文类型：会议论文
发表时间：2016-01-01
收录刊物：CPCI-S
页面范围：775-775
关键字：Biomedical event extraction; Distributed representation; Deep learning; Convolutional neural network
摘要：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.
