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结合多特征嵌入和多网络融合的中文医疗命名实体识别

雷松泽 刘博 王瑜菲 单奥奎

雷松泽, 刘博, 王瑜菲, 单奥奎. 结合多特征嵌入和多网络融合的中文医疗命名实体识别[J]. 电子与信息学报, 2023, 45(8): 3032-3039. doi: 10.11999/JEIT220802
引用本文: 雷松泽, 刘博, 王瑜菲, 单奥奎. 结合多特征嵌入和多网络融合的中文医疗命名实体识别[J]. 电子与信息学报, 2023, 45(8): 3032-3039. doi: 10.11999/JEIT220802
LEI Songze, LIU Bo, WANG Yufei, SHAN Aokui. Chinese Medical Named Entity Recognition Combined with Multi-Feature Embedding and Multi-Network Fusion[J]. Journal of Electronics & Information Technology, 2023, 45(8): 3032-3039. doi: 10.11999/JEIT220802
Citation: LEI Songze, LIU Bo, WANG Yufei, SHAN Aokui. Chinese Medical Named Entity Recognition Combined with Multi-Feature Embedding and Multi-Network Fusion[J]. Journal of Electronics & Information Technology, 2023, 45(8): 3032-3039. doi: 10.11999/JEIT220802

结合多特征嵌入和多网络融合的中文医疗命名实体识别

doi: 10.11999/JEIT220802
基金项目: 新型网络与检测控制国家地方联合工程实验室基金(GSYSJ2016008)
详细信息
    作者简介:

    雷松泽:男,博士,副教授,研究方向为深度学习、模式识别等

    刘博:女,硕士生,研究方向为深度学习等

    王瑜菲:女,硕士生,研究方向为深度学习等

    单奥奎:男,硕士生,研究方向为深度学习等

    通讯作者:

    刘博 liubo0909888@163.com

  • 11) https://github.com/google-research/bert.
  • 22) https://pinyin.sogou.com/dict/cate/index/132?rf=dictindex.3) http://tool.httpcn.com/Zi/.4) https://openhownet.thunlp.org/.
  • 中图分类号: TP391.1; R-05

Chinese Medical Named Entity Recognition Combined with Multi-Feature Embedding and Multi-Network Fusion

Funds: The National Joint Engineering Laboratory of New Network and Detection Foundation (GSYSJ2016008)
  • 摘要: 在医疗领域中,实体识别能够从大规模电子病历文本中提取有价值信息,由于缺乏定位实体边界的特征以及存在语义信息提取不完整等问题,中文的命名实体识别(NER)实现更加困难。该文提出一种针对中文电子病历的结合多特征嵌入和多网络融合的模型(MFE-MNF)。该模型嵌入多粒度特征,即字符、单词、部首和外部知识,扩展字符的特征表示,明确实体边界。将特征向量分别输入到双向长短期记忆神经网络(BiLSTM)和该文构建的自适应图卷积网络等双通路中,全面深入地捕获上下文语义信息和全局语义信息,缓解语义信息提取不完整问题。在CCKS2019和CCKS2020数据集上进行实验验证,结果表明,相比于传统实体识别模型,该文模型能够准确且有效地提取实体。
  • 图  1  知识嵌入模块

    图  2  基于多特征嵌入的字符表示

    图  3  “入院后诊断为阑尾炎”的语义树

    图  4  中文电子病历标注结果

    图  5  训练结果

    表  1  实验参数设置

    参数名数值单位
    字符嵌入维度768
    GCN层数2
    滑动窗口大小10字符
    Dropout0.500
    Batch_size64
    Epoch80
    学习率0.001
    下载: 导出CSV

    表  2  各模型在CCKS2019数据集上的比较结果(%)

    模型PRF1
    Word2vec-BiLSTM-CRF[5]80.7480.4280.59
    Bert-BiLSTM-CRF[21]82.4581.8682.08
    ME-CNER[6]83.5682.9183.13
    Lattice LSTM[19]84.4483.8984.18
    Bert-GCN-CRF[20]85.0584.1484.65
    MFE-MNF85.3184.9685.15
    下载: 导出CSV

    表  3  各模型在CCKS2020数据集上的比较结果(%)

    模型PRF1
    Word2vec-BiLSTM-CRF[5]87.1686.7786.97
    Bert-BiLSTM-CRF[19]88.7888.3588.61
    ME-CNER[6]90.1090.1790.15
    Lattice LSTM[20]91.1090.4190.54
    Bert-GCN-CRF[21]91.1990.9190.96
    MFE-MNF91.4591.0991.21
    下载: 导出CSV

    表  4  各模型的计算复杂度和计算时间的比较结果

    模型参数量(M)计算量(M)时间(s)
    Word2vec-BiLSTM-CRF[5]17264.49
    Bert-BiLSTM-CRF[21]1242001.97
    ME-CNER[6]15233.36
    Lattice LSTM[19]47785.33
    Bert-GCN-CRF[20]1262034.54
    MFE-MNF1051763.21
    下载: 导出CSV

    表  5  嵌入模块的消融实验(%)

    模型PRF1
    character87.9387.5887.77
    + word89.2988.5189.08
    + radical89.7489.3389.52
    + sememe90.0589.6289.85
    + word + radical90.4390.0990.28
    + word + sememe91.0190.3790.74
    +character+sememe+radical+word91.4591.0991.21
    下载: 导出CSV

    表  6  语义信息提取模块的消融实验(%)

    模型PRF1
    BiLSTM+AGCN91.4591.0991.21
    - BiLSTM90.1389.8590.04
    - AGCN89.8989.4289.65
    下载: 导出CSV

    表  7  基于CCKS2019数据集的词典与覆盖率实验(%)

    实体是否出现在训练集没有词典有词典
    PRF1PRF1
    全部出现90.6990.0390.3891.4591.0991.21
    部分出现88.2887.6087.9288.9988.2388.62
    不出现86.8886.7786.8587.6087.0987.29
    下载: 导出CSV

    表  8  基于CCKS2020数据集的词典与覆盖率实验(%)

    实体是否出现在训练集没有词典有词典
    PRF1PRF1
    全部出现85.2884.5784.9285.3184.9685.15
    部分出现82.8281.1481.4683.5382.7783.13
    不出现81.4280.3180.7782.1481.6381.83
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-06-17
  • 修回日期:  2022-12-02
  • 网络出版日期:  2022-12-08
  • 刊出日期:  2023-08-21

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