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Indexed by:会议论文

Date of Publication:2017-01-01

Included Journals:CPCI-S、Scopus

Volume:2017-January

Page Number:445-450

Key Words:biomedical events; trigger detection; convolutional neural network; bidirectional LSTM; CRF

Abstract: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.