[1]陈 琛,刘小云,方玉华.融合注意力机制的电子病历命名实体识别[J].计算机技术与发展,2020,30(10):216-220.[doi:10. 3969 / j. issn. 1673-629X. 2020. 10. 038]
　CHEN Chen,LIU Xiao-yun,FANG Yu-hua.Named Entity Recognition in Electronic Medical Record Introducing Attention Mechanisms[J].,2020,30(10):216-220.[doi:10. 3969 / j. issn. 1673-629X. 2020. 10. 038]
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融合注意力机制的电子病历命名实体识别()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
30
期数:
2020年10期
页码:
216-220
栏目:
应用开发研究
出版日期:
2020-10-10

文章信息/Info

Title:
Named Entity Recognition in Electronic Medical Record Introducing Attention Mechanisms
文章编号:
1673-629X(2020)10-0216-05
作者:
陈 琛; 刘小云; 方玉华
厦门医学院信息中心,福建 厦门 361023
Author(s):
CHEN Chen; LIU Xiao-yun; FANG Yu-hua
Information Technology Center,Xiamen Medical College,Xiamen 361023,China
关键词:
命名实体识别; 注意力机制; 电子病历; 双向长短期记忆神经网络; 条件随机场
Keywords:
named entity recognition; attention mechanisms; electronic medical records; BiLSTM; CRF
分类号:
TP391. 1
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 10. 038
文献标志码:
A
摘要:
命名实体识别是自然语言处理中的一项基础性关键任务,基于电子病历命名实体识别是临床决策支持和医疗知 识图谱构建等任务的基础。 针对传统的双向长短时记忆神经网络(bi-directional long short-term memory,BiLSTM)结合 条件随机场(conditional random field,CRF)的 BiLSTM-CRF 模型在处理医疗文本命名实体识别问题时面临的文本特征提 取不够充分和未登录词不能充分识别等问题,引入注意力机制(attention mechanisms),提出一种基于注意力机制的 BiLSTM-CRF 命名实体识别模型。 该模型以字向量作为神经网络的输入,BiLSTM 层建模上下文信息,捕捉双向的语义 依赖; ATTENTION 层重点关注输入数据中显著的与当前输出相关的特征,抑制无用信息;CRF 层充分考虑了句子级别的 标签依赖信息,对整个句子进行解码预测输出。 实验结果表明,在电子病历的命名实体识别中,该模型较传统模型提升了 一定的识别效果。
Abstract:
Named entity recognition is a basic key task in natural language processing,and named entity recognition based on electronic medical records is? the basis of tasks such as clinical decision support and medical knowledge graph. For the problems that the traditional bi-directional BiLSTM-CRF model combined long-short-term memory(BiLSTM) and conditional random field (CRF) is faced with insufficient extraction of text features and insufficient recognition of unregistered words when dealing with the recognition of medical text naming entities,attention mechanisms are introduced to propose a BILSTM-CRF named entity recognition model based on the attention mechanism. The model takes the word vector as the input of the neural network,modeling context information on the BiLSTM layer to capture the bidirectional semantic dependence. The ATTENTION layer focuses on the salient features of the input data related to the current output and suppresses the useless information. The CRF layer fully considers the label dependency information at the sentence level to decode the prediction output for the whole sentence. The experiment shows that this model is better than the traditional model in the recognition of named entity identification in electronic medical records.

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更新日期/Last Update: 2020-10-10