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基于BiGRU神经网络的基因和表型的命名实体识别

Kangjie Wu¹, Liqian Xu¹, Xinxiang Li¹

¹School of Information and Computer, Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, China.

Computational biology and chemistry

|November 23, 2023

概括

本研究介绍了一种改进的双向封闭循环单元神经网络 (BI-GRU-SF),用于米基因和表型的命名实体识别 (NER). 该模型增强了从作物文献中提取信息,有助于开发更高质量的作物.

科学领域:

农业科学农业科学
计算生物学计算生物学
自然语言处理自然语言处理.

背景情况:

命名实体识别 (NER) 对于农业中处理大型数据集至关重要.
从文本中识别大米基因和表型对于作物改进研究至关重要.
现有的NER方法在准确提取特定生物实体方面面临挑战.

研究的目的:

开发一种用于识别大米基因和表型实体的自动化方法.
提高从与大米有关的文献和专利中提取信息的准确性和效率.
为农作物科学领域的研究人员提供一个有价值的工具.

主要方法:

开发了一种改进的双向封闭循环单元神经网络 (BI-GRU).
该BI-GRU模型与Softmax函数相结合,创建了BI-GRU-SF模型.
深度学习使语义信息学习能够在没有手动功能工程的情况下实现.

主要成果:

与现有模型相比,BI-GRU-SF模型在命名实体识别任务中表现出更高的性能.
该模型有效地从大米文献和专利中识别了基因名称和表型.
实验结果验证了模型的增强功能.

结论:

拟议的BI-GRU-SF模型为大米基因和表型识别NER提供了显著的进步.
这种技术促进了有效的信息获取,支持研究更高质量的作物.
开发的模型和相关资源是公开可用于学术用途.

关键词:

这是一个巨大的BIGRU.深度学习是一种深度学习.命名实体认可命名实体认可

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