基于CRF与规则相结合的中文电子病历命名实体识别研究  

A study on the named entity recognition of Chinese electronic medical record based on combination of CRF and rules

作　　者：翟菊叶[1] 陈春燕[1] 张钰[1] 陈玉娥[1] 刘玉文[1] 

机构地区：[1]蚌埠医学院,安徽蚌埠233030

出　　处：《包头医学院学报》2017年第11期124-125,130,共3页Journal of Baotou Medical College

基　　金：安徽省高校自然科学一般项目(KJ2015B076by);安徽省质量工程项目(2016mooc256);安徽高校人文社科重点项目(SK2017A0182);蚌埠医学院自然科学基金面上项目(BYKY1659)

摘　　要：目的:探讨基于条件随机场(conditional random field,CRF)与规则相结合的中文电子病历命名实体识别。方法:基于条件随机场和规则相结合的方法来识别实体,将语言、关键词、词典等作为特征,识别出的结果再利用规则进行优化。结果:与条件随机场的方法相比,条件随机场和规则相结合的方法识别准确率提高到78.98%,召回率和F值也提高到88.37%和83.41%。结论:基于条件随机场和规则相结合的方法来识别实体,准确率和召回率满足应用需求,为电子病历后续研究奠定了基础。Objective：To explore the named entity recognition of Chinese electronic medical record based on the combination of conditional random field （CRF） and rules. Methods：Entities are recognized based on the combination of conditional random field and rules. Language, keywords, dictionaries are used as recognition features, and the recognition results are optimized by the rules. Results：Compared with the method of conditional random field, the accuracy of the method combining the conditional random field with the rules is improved to 78.98%, and the recall rate and F value are also increased to 88.37% and 83.41%. Conclusion： The accuracy and recall rate based on the method combining the conditional random field with the rule to identify entities can meet the apphcation requirements, which will lay the foundation for the follow - up study of electronic medical record.

关 键 词：命名实体识别 条件随机场 规则 

分 类 号：R197.3[医药卫生—卫生事业管理] TP391.1[医药卫生—公共卫生与预防医学]