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基于字典的匹配图形网络用于生物医学命名实体识别.

Yinxia Lou1, Xun Zhu2, Kai Tan3

  • 1School of Artificial Intelligence, Jianghan University, Wuhan, 430056, China.

Scientific reports
|December 8, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种基于字典的匹配图形网络,用于生物医学命名实体识别 (BioNER). 新模型通过充分利用词典信息显著提高了BioNER的性能,超过了当前最先进的方法.

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科学领域:

  • 生物医学信息学是生物医学信息学.
  • 自然语言处理自然语言处理.
  • 机器学习 机器学习

背景情况:

  • 生物医学命名实体识别 (BioNER) 对于分析生物医学文本至关重要.
  • 深度神经网络对于BioNER来说很常见,经常使用生物医学字典.
  • 现有的词典方法具有性能限制.

研究的目的:

  • 提出一个新的基于字典的匹配图形网络,以增强BioNER.
  • 改进生物医学词典信息在实体识别中的利用.

主要方法:

  • 为BioNER开发了一个基于字典的匹配图形网络.
  • 预测的字典组合在一个定向图上.
  • 将匹配图与双向图形卷积网络 (BiGCN) 集成.

主要成果:

  • 在五个Bio-NER数据集上取得了显著的F1得分改善.
  • 与最先进的模型相比,表现出卓越的性能.
  • 具体的改善包括2.8%的BC2GM,1.3%的BC4CHEMD,1.1%的BC5CDR,1.6%的NCBI疾病和0.5%的JNLPBA.

结论:

  • 拟议的基于字典的匹配图形网络有效地识别生物医学命名实体.
  • 这种方法为简单的掩盖词典方法提供了更好的替代方案.
  • 该模型显示了推动生物医学信息分析的巨大潜力.