LSTM-Based End-to-End Framework for Biomedical Event Extraction
| Autor: | Xinyi Yu, Zhang Xiong, Wenge Rong, Jingshuang Liu, Yuanxin Ouyang, Deyu Zhou |
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| Rok vydání: | 2020 |
| Předmět: |
Biomedical Research
Parsing Dependency (UML) Event (computing) Computer science Applied Mathematics Feature extraction Computational Biology Semantics computer.software_genre Task (project management) End-to-end principle Genetics Task analysis Data Mining Neural Networks Computer Data mining computer Biotechnology |
| Zdroj: | IEEE/ACM Transactions on Computational Biology and Bioinformatics. 17:2029-2039 |
| ISSN: | 2374-0043 1545-5963 |
| DOI: | 10.1109/tcbb.2019.2916346 |
| Popis: | Biomedical event extraction plays an important role in the extraction of biological information from large-scale scientific publications. However, most state-of-the-art systems separate this task into several steps, which leads to cascading errors. In addition, it is complicated to generate features from syntactic and dependency analysis separately. Therefore, in this paper, we propose an end-to-end model based on long short-term memory (LSTM) to optimize biomedical event extraction. Experimental results demonstrate that our approach improves the performance of biomedical event extraction. We achieve average F1-scores of 59.68, 58.23, and 57.39 percent on the BioNLP09, BioNLP11, and BioNLP13's Genia event datasets, respectively. The experimental study has shown our proposed model's potential in biomedical event extraction. |
| Databáze: | OpenAIRE |
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