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Indexed by:会议论文

Date of Publication:2015-11-09

Included Journals:EI、CPCI-S、Scopus

Page Number:429-432

Key Words:biomedical event extraction; Trigger Detection; Dependency-based word embedding; neural network

Abstract:Biomedical events can reveal crucial processes in biomedical research. As an important step in biomedical event extraction, biomedical event trigger detection has become a research hotspot. Traditional machine learning methods, which aim to manually design powerful features fed to the classifiers, greatly depend on the understanding of the specific task. In this paper, we propose an approach to automatically learn good features from raw input without manual intervention. The approach is based on dependency-based word embedding and first learns dependency-based word embedding from all available PubMed abstracts. The word embedding contains rich functional and semantic information. Then neural network architecture is used to learn better feature representation based on raw dependency-based word embedding. Meanwhile, we dynamically adjust the embedding while training for adapting to the trigger classification task. Finally, softmax classifier labels the examples by specific trigger class using the features learned by the model. The experimental results show that our approach achieves a micro Fl score of 78.27% and a macro Fl score of 76.94% in significant trigger classes, and performs better than baseline methods. In addition, we can achieve the semantic distributed representation of every trigger word.