Scholarlink

<p>Named entity recognition for Chinese electronic medical records often suffers from insufficient feature representation due to inadequate integration of global contextual information and local semantic patterns. To address this issue, this paper proposes Named Entity Recognition based on the fusion of global and local semantic features for the Chinese electronic medical record(NERF). NERF employs bidirectional gated recurrent units to capture contextual semantics, a self-attention mechanism to model global dependencies, and a multilayer perceptron to perform nonlinear feature transformation on token-level representations. The global features and the local features are fused via addition and decoded using a conditional random field. In addition, adversarial training is used to improve robustness and generalization. The comparison experimental results on the CCKS2017 dataset shows that NERF outperforms all compared methods. Its precision, recall, and F1-score are 90.89%, 94.49%, and 92.65%, respectively.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Named entity recognition based on fusion of global and local semantic features for Chinese electronic medical records

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月28日

查看全文
  • Kexin Luo,
  • Wei Yang,
  • Xiaowen Chen,
  • Yang Wang,
  • Beike Zhu,
  • Guanci Yang,
  • Ling He

摘要

Named entity recognition for Chinese electronic medical records often suffers from insufficient feature representation due to inadequate integration of global contextual information and local semantic patterns. To address this issue, this paper proposes Named Entity Recognition based on the fusion of global and local semantic features for the Chinese electronic medical record(NERF). NERF employs bidirectional gated recurrent units to capture contextual semantics, a self-attention mechanism to model global dependencies, and a multilayer perceptron to perform nonlinear feature transformation on token-level representations. The global features and the local features are fused via addition and decoded using a conditional random field. In addition, adversarial training is used to improve robustness and generalization. The comparison experimental results on the CCKS2017 dataset shows that NERF outperforms all compared methods. Its precision, recall, and F1-score are 90.89%, 94.49%, and 92.65%, respectively.