融入对抗训练的中文电子病历命名实体识别

Named Entity Recognition of Chinese Electronic Medical Record Integrated with Confrontation Training

为提高中文电子病历中命名实体识别模型鲁棒性和准确性,为此提出一种基于BERT模型融入对抗网络的中文电子命名实体识别模型,该方法使用BERT预训练模型动态生成字向量,通过对抗训练生成扰动,将字向量与扰动相加生成对抗样本,再通过膨胀卷积网络(IDCNN)捕捉句子单词间的依赖,最后通过条件随机场(CRF)得到最终预测结果。在CCKS2019数据集上的实验表明,模型的F1值达到83.19%,证明该模型的有效性。

In order to improve the robustness and accuracy of the named entity recognition model in Chinese electronic medical records,a Chinese electronic named entity recognition model based on the BERT model and the confrontation network is proposed.The method uses the BERT pre-training model to dynamically generate the word vector,generates the disturbance through the confrontation training,adds the word vector and the disturbance to generate the confrontation sample,and then captures the dependency between the words in the sentence through the Iterated Dilated Con-volutional Neural Network(IDCNN).Finally,the final prediction result is obtained by Conditional Random Field(CRF).The experiment on CCKS 2019 dataset shows that the F1 value of the model reaches 83.19%,which proves the effectiveness of the model.