期刊
INFORMATION SCIENCES
卷 512, 期 -, 页码 175-185出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2019.09.075
关键词
Biomedical event; Trigger detection; Deep learning; Neural network; Syntactic features
资金
- National Natural Science Foundation of China [61702121, 61772378]
- Research Foundation of Ministry of Education of China [181ZD015]
- Major Projects of the National Social Science Foundation of China [11ZD189]
- Science and Technology Project of Guangzhou [201704030002]
- Bidding Project of GDUFS Laboratory of Language Engineering and Computing [LEC2018ZBKT004]
Biomedical event trigger detection is a heated research topic since its important role in biomedical event extraction. Previous studies show that syntactic features are very crucial for the task. However, existing methods largely focus on traditional statistical models, and usually capture syntactic features by extracting a set of manually-crafted features based on dependency tree. This limits the performance of the task. In this paper, we propose a tree-based neural network model, which can automatically learn syntactic features from dependency tree for trigger detection. Specifically, we use a recursive neural network to represent whole dependency tree globally, to better incorporate dependency-based syntax information. Results on MLEE and BioNLP-09 datasets show that the proposed model achieves 80.28% and 73.24% Fl score, respectively, outperforming traditional statistical models and neural baseline systems. (C) 2019 Elsevier Inc. All rights reserved.
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