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论文类型：会议论文

发表时间：2017-01-01

收录刊物：CPCI-S、Scopus

卷号：2017-January

页面范围：445-450

关键字：biomedical events; trigger detection; convolutional neural network; bidirectional LSTM; CRF

摘要：Trigger detection plays a key role in the extraction of biomedical events, so it will influence the results of biomedical events extraction directly. The traditional biomedical event trigger recognition method is based on artificial design features and construct feature vectors; Not only does it consume great amounts of manpower, it also lacks system generalization ability. Most of methods of trigger detection are based on the convolutional neural network that identify each word in the text, and regard it as a multi-classification task. However for the multi-word composed of the trigger, there is no useful recognition effect. In this paper, we will use the IBO format and consider the trigger detection as a task of sequence annotation, a solution that improves the recognition accuracy of multi-word triggers by bidirectional LSTM and CRF.