面向中医电子病历的症状实体及属性抽取  

Symptom Entity and Attribute Extraction for TCM Electronic Medical Record

作　　者：胡定兴 杜建强[1] 石强 罗计根 刘勇 HU Dingxing;DU Jianqiang;SHI Qiang;LUO Jigen;LIU Yong(School of Computer,Jiangxi University of Chinese Medicine,Nanchang 330004,China;Qihung Medical College,Jiangxi University of Chinese Medicine,Nanchang 330004,China)

机构地区：[1]江西中医药大学计算机学院,江西南昌330004 [2]江西中医药大学岐黄国医书院,江西南昌330004

出　　处：《现代信息科技》2022年第3期70-75,共6页Modern Information Technology

基　　金：国家重点研发计划(2019YFC12301);国家自然科学基金项目(6214120,82160955);江西省自然科学基金(20202BAB202019);江西省教育厅科技项目(GJJ190863);江西省一流学科建设科研启动基金专项项目(SYLXK-ZHYI060)。

摘　　要：文章针对中医临床症状实体及属性抽取存在医疗短文本语义信息欠缺,常用的流水线方法易导致多任务之间产生错误累积的问题,提出一种基于深度学习的症状实体及属性抽取方法。首先通过基于BLSTM-CRF的序列标注模型完成“实体/修饰属性”识别;其次根据扩展步长的就近匹配原则生成高覆盖率、低冗余度的“实体—属性值”候选对;最后基于ERNIEBGRU-MP完成关系分类,利用ERNIE丰富文本上下文信息,联合BGRU提取文本全局特征信息,采用最大池化法过滤冗余和噪声信息,提高模型的泛化性和鲁棒性。Aiming at the problem of the lack of semantic information of medical short texts in entity and attribute extracting of TCM clinical symptoms and the accumulation of errors among multiple tasks coursed by common pipeline methods.A symptom entity and attribute extracting method based on deep learning is proposed.Firstly,the recognition of“entity/modification attribute”is completed by the sequence annotation model based on BLSTM-CRF;Secondly,“entity-attribute value”candidate pairs with high coverage and low redundancy are generated according to the nearest matching principle of extended step size;finally,the relationship classification is completed based on ERNIE-BGRU-MP,Ernie is used to enrich the text context information,and the max-pooling method is used to filter the redundant and noise information,so as to improve the generalization and robustness of the model.

关 键 词：实体及属性抽取 ERNIE BGRU 最大池化 中医药信息学 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]