摘要
针对传统的基于模板匹配、人工构建特征、语义匹配等解决术语标准化的方案,往往会存在术语映射准确率不高,难以对齐等问题.本文结合医疗领域的文本中术语口语化、表达多样化的特点,使用了多策略召回和蕴含语义评分排序模块来提升医学术语标准化效果.在多策略召回模块中使用了基于Jaccard相关系数、TF-IDF、历史召回方法进行召回,在蕴含语义评分模块使用了RoBERTa-wwm-ext作为判分语义模型.首次在医学专业人员标注的基于SNOMED CT标准的中文数据集上验证了可用性.实验证明,在医疗知识特征的处理中,本方法能够在医学术语标准化实际应用上达到不错的效果,具有很好的泛化性及实用价值.
Traditional terminology standardization schemes based on template matching,artificially constructed features,semantic matching,etc.,are often faced with problems such as low terminology mapping accuracy and difficult alignment.Given the colloquial and diverse expression of terminology in medical texts,modules of multi-strategy recall and implication semantic score ranking are used to improve the effect of medical terminology standardization.In the multi-strategy recall module,the recall method based on the Jaccard correlation coefficient,term frequencyinverse document frequency(TF-IDF),and historical recalls is employed.In the implication semantic scoring module,RoBERTa-wwm-ext is adopted as the scoring semantic model.The usability of the proposed method is validated for the first time on a Chinese dataset that is based on the systematized nomenclature of medicine-clinical terms(SNOMED CT)standard and annotated by medical professionals.Experiments show that in the processing of medical knowledge features,the proposed method can achieve favorable results in practical applications of medical terminology standardization and has high generalization and practical value.
作者
韩振桥
付立军
刘俊明
郭宇捷
唐珂轲
梁锐
HAN Zhen-Qiao;FU Li-Jun;LIU Jun-Ming;GUO Yu-Jie;TANG Ke-Ke;LIANG Rui(Shenyang Institute of Computing Technology,Chinese Academy of Sciences,Shenyang 110168,China;University of Chinese Academy of Sciences,Beijing 100049,China;Laboratory of Big Data and Artificial Intelligence Technolgy,Shandong University,Jinan 250100,China;Sinohealth Technology Limited,Guangzhou 510620,China)
出处
《计算机系统应用》
2022年第10期245-253,共9页
Computer Systems & Applications
基金
国家社科基金(21BTQ106)
