A biomedical event extraction method based on fine-grained and attention mechanism

Biomedical event extraction is a fundamental task in biomedical text mining, which provides inspiration for medicine research and disease prevention. Biomedical events include simple events and complex events. Existing biomedical event extraction methods usually deal with simple events and complex e...

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Bibliographic Details
Published in:BMC bioinformatics 2022-07, Vol.23 (1), p.308-17, Article 308
Main Authors: He, Xinyu, Tai, Ping, Lu, Hongbin, Huang, Xin, Ren, Yonggong
Format: Article
Language:English
Subjects:
Analysis
Argument detection
Bacteria
Data mining
Data Mining - methods
Datasets
Deep learning
Event extraction
Fine-grained
Gene expression
Long short-term memory
Machine learning
Medical research
Medicine, Experimental
Methods
Multi-level attention
Neural networks
Performance enhancement
Semantics
Sparsity
Trigger identification
Tumorigenesis
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Summary:Biomedical event extraction is a fundamental task in biomedical text mining, which provides inspiration for medicine research and disease prevention. Biomedical events include simple events and complex events. Existing biomedical event extraction methods usually deal with simple events and complex events uniformly, and the performance of complex event extraction is relatively low. In this paper, we propose a fine-grained Bidirectional Long Short Term Memory method for biomedical event extraction, which designs different argument detection models for simple and complex events respectively. In addition, multi-level attention is designed to improve the performance of complex event extraction, and sentence embeddings are integrated to obtain sentence level information which can resolve the ambiguities for some types of events. Our method achieves state-of-the-art performance on the commonly used dataset Multi-Level Event Extraction. The sentence embeddings enrich the global sentence-level information. The fine-grained argument detection model improves the performance of complex biomedical event extraction. Furthermore, the multi-level attention mechanism enhances the interactions among relevant arguments. The experimental results demonstrate the effectiveness of the proposed method for biomedical event extraction.
ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-022-04854-0