[1]张聪品,方滔,刘昱良.基于LSTM－CＲF命名实体识别技术的研究与应用[J].计算机技术与发展,2019,29(02):106-108.[doi:10．3969/j．issn．1673－629X．2019．02．022]
　ZHANG Congpin,FANG Tao,LIU Yuliang.Research and Application of Named Entity Recognition Based on LSTM－CＲF[J].,2019,29(02):106-108.[doi:10．3969/j．issn．1673－629X．2019．02．022]
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基于LSTM－CＲF命名实体识别技术的研究与应用()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年02期
页码:
106-108
栏目:
智能、算法、系统工程
出版日期:
2019-02-10

文章信息/Info

Title:
Research and Application of Named Entity Recognition Based on LSTM－CＲF
文章编号:
1673－629X(2019)02－0106－03
作者:
张聪品; 方滔; 刘昱良
河南师范大学 计算机与信息工程学院，河南 新乡 453007
Author(s):
ZHANG Cong－pin; FANG Tao; LIU Yu－liang
School of Computer and Information Engineering，Henan Normal University，Xinxiang 453007，China
关键词:
长短时记忆神经网络; 条件随机场; 命名实体; 电子病历
Keywords:
long and short time memory neural network; conditional random field; named entity; electronic medical record
分类号:
TP319
DOI:
10．3969/j．issn．1673－629X．2019．02．022
摘要:
随着深度神经网络的发展，深度学习不仅占据了模式识别等领域的统治地位，而且已应用到自然语言处理的各个方面，如中文命名实体识别。对电子病历中的命名实体进行识别时，构建了内嵌条件随机场的长短时神经网络模型，使用长短时神经网络隐含层的上下文向量作为输出层标注的特征，使用内嵌的条件随机场模型表示标注之间的约束关系。该模型识别出了电子病历中的身体部位、疾病名称、检查、症状和治疗五类实体，准确率达到 96．29%，精确率达到了 91. 61%，召回率 96．22%，F 值93．85，其中症状这一实体类别，精确率达到 96．08%，召回率 98．98%，F 值 97．51。实验结果表明，内嵌条件随机场的长短时记忆神经网络模型在识别中文命名实体方面是有效的，有助于自动抽取中文电子病历中实体之间的关系、构建医疗知识图谱。
Abstract:
With the development of deep neural network，deep learning not only occupies the dominant position in pattern recognition and other fields，but also has been applied to various aspects of natural language processing，such as Chinese named entity recognition． Whenrecognizing named entities in electronic medical records，we construct a long and short time neural network model with embedded randomfield． The context vector of the hidden layer of long and short time neural networks is used as the feature of output layer annotation，andthe embedded conditional random field model to represent the constraint relationship between the annotations． The model identifies fivetypes of entities，including body parts，disease name，examination，symptom and treatment in the electronic medical record，with accuracyof 96．29%，precision rate of 91．61%，recall rate of 96．22%，and F value of 93．85． For the entity category of symptom，the precision ratereaches 96．08%，recall rate of 98．98%，F value of 97．51． The experiment shows that the proposed model is effective in identifying Chi-nese named entities，which is helpful for the automatic extraction of the relationship between entities in Chinese electronic medical records and the construction of medical knowledge maps．

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更新日期/Last Update: 2019-02-10