期刊论文

Named entity recognition in the field of diabetes based on double-layer Bi-LSTM-CRF model

中文

邵阳学院学报(自然科学版)

1672-7010

2020

17

1

21-26

10.3969/j.issn.1672-7010.2020.01.004

湖南省教育厅科研项目(17C0293)。

本校为其他机构

随着信息技术的发展,电子文档在糖尿病领域的信息记录中得到了大量应用,通过自动化技术对这些电子文档进行分析具有重大的意义。由于现有的命名实体识别方法在糖尿病领域中识别准确率偏低。为了改变这种现状,提出了双层的双向长短时记忆神经网络条件随机场模型(Bi-LSTM-CRF),并将其应用到糖尿病领域命名实体识别任务中。实验结果表明该模型在包含15种实体类别的数据集上准确率达到了89.14%,且在外部测试集上平均F 1值为72.89%,充分揭示了双层Bi-LSTM-CRF模型的有效性。

With the development of information technology,electronic documents have been widely used in the information record of diabetes.Analysis of these electronic documents through automation technology has a great significance.Due to the low accuracy of existing named entity recognition methods in the field of diabetes,a double-layer bidirectional long-short-term memory neural network conditional random field model(Bi-LSTM-CRF)was proposed and applied to the task of named entity recognition in the field of diabetes.Experimental results show that the accuracy of the model is 89.14%on a dataset conta...