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作 者:刘耀[1] 翟雨 周家辉 Liu Yao;Zhai Yu;Zhou Jiahui(Information Technology Support Center,Institute of Scientific&Technical Information of China,Beijing 100038,China;Dept.of Chinese&Bilingual Study,The Hong Kong Polytechnic University,Hong Kong 999077,China)
机构地区:[1]中国科学技术信息研究所信息技术支持中心,北京100038 [2]香港理工大学中文及双语系,中国香港999077
出 处:《计算机应用研究》2025年第4期1143-1149,共7页Application Research of Computers
基 金:国家社会科学基金资助项目(21BTQ011)。
摘 要:对医学书籍等领域文本与患者自述等患者相关文本结构化解析,能够为问诊系统构建提供结构化资源支撑,有助于辅助医生诊断。为此,提出一种文本结构化解析方法。该方法利用工程方式获取领域先验知识与训练数据;基于BERT模型,通过掩码语言模型与实体排序任务将医学领域先验知识融入模型,获得先验知识增强的MedReBERT模型;设计文本生成模板构造概念标引模型与概念关系标引模型,实现概念及关系的标引,并提出一种工程处理与算法协同学习框架。实验证明该方法能够实现问诊系统文本的结构化解析。提出的方法框架能够建立各算法间的接口,实现问诊系统文本结构化解析相关算法的低门槛迭代应用。Structured parsing of domain texts such as medical books and patient-related texts such as patients’statements can provide structured resource support for the construction of medical consultation system,which can help assist doctors in diagnosis.Therefore,this paper proposed a method for structured parsing.The method used engineering approaches to obtain domain-specific prior knowledge and training data.Based on the BERT model,the method integrated medical prior knowledge into the model through masked language model and entity ranking tasks to obtain the MedReBERT model enhanced by the prior knowledge.The method designed the text generation template to train concept indexing and concept relationship indexing mo-dels,to realize the indexing of concepts and relationships.This paper proposed a collaborative framework that combined engineering processes with algorithmic learning.The experimental results show that the method can realize the structured parsing of the text.The proposed framework can establish interfaces between algorithms and realize the low-threshold iterative application of algorithms related to the structured parsing of text in the consultation system.
分 类 号:TP182[自动化与计算机技术—控制理论与控制工程]
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