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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >LSTM-Based End-to-End Framework for Biomedical Event Extraction
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LSTM-Based End-to-End Framework for Biomedical Event Extraction

机译：基于LSTM的生物医学事件提取的端到端框架

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摘要

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.
机译：生物医学事件提取在大规模科学出版物的提取生物信息中起着重要作用。但是，大多数最先进的系统将此任务分为几个步骤，这导致级联错误。此外，它很复杂地分别生成句法和依赖性分析的特征。因此，在本文中，我们提出了一种基于长短期存储器（LSTM）的端到端模型来优化生物医学事件提取。实验结果表明，我们的方法提高了生物医学事件提取的性能。我们分别在Bionlp09，Bionlp11和Bionlp13的Genia事件数据集中达到59.68,58.23和57.39％的平均f1分数。实验研究表明我们提出的模型在生物医学事件提取中的潜力。

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