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融合依存句法和实体信息的临床时间关系抽取

Extraction of Clinical Temporal Relation Fusing Dependency Syntax and Entity Information
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摘要 在临床文本中,时间关系对于研究患者的病情和治疗方案至关重要。而目前的时间关系抽取基于简单时间比较,仅判断4种时间关系。考虑中文临床文本中还存在大量的复杂时间和关系,现有时间关系抽取任务不能全部表达临床事件的时间关系,参考CTO时间本体将抽取任务扩展为复杂时间关系抽取。同时针对中文临床文本语义的复杂性,提出了融合依存句法和实体信息的模型学习中文句子的整体信息和实体信息。该模型针对句内时间关系和句间时间关系设计依存特征矩阵引导BERT的编码器聚合全局信息和局部信息,然后导出句子表征向量,在此基础上使用内积和哈达玛积提取丰富的实体信息,最终将句子信息和实体信息导入分类器判断时间关系。与基线模型和其他深度学习模型相比,证明了该模型的有效性。 In clinical texts,temporal relation is crucial to the study of patient's conditions and treatment options.The current temporal relation extraction is based on the simple temporal comparison,and only four temporal relations are judged.Considering that there are still a large number of complex times and relations in Chinese clinical texts,the existing temporal relation extraction task cannot fully express the temporal relation of clinical events,referring to the CTO temporal ontology,the extraction task is expanded to complex time relationship extraction.At the same time,aiming at the semantic complexity of Chinese clinical texts,a model integrating dependency syntax and entity information is proposed to learn the overall information and entity information of Chinese sentences.The model scrambles to design dependency feature matrices for intra-sentence temporal relation and inter-sentence temporal relation to guide BERT's encoder to aggregate global and local information,derive sentence representation vectors on which rich entity information is extracted using the inner product and Hadamard product.Finally,the sentence information and entity information is imported into the classifier to determine temporal relation.Compared with baseline model and other deep learning model,the effectiveness of the proposed model is demonstrated.
作者 黄汉琴 顾进广 符海东 HUANG Han-qin;GU Jin-guang;FU Hai-dong(School of Computer Science,Wuhan University of Science and Technology,Wuhan 430065,China;Key Laboratory of Rich-media Knowledge Organization and Service of Digital Publishing Content National Press and Publication Administration of the People’s Republic of China,Beijing 100038,China;Institute of Big Data Science and Engineering,Wuhan University of Science and Technology,Wuhan 430065,China)
出处 《计算机技术与发展》 2024年第1期128-135,共8页 Computer Technology and Development
基金 国家自然科学基金委员会,联合基金项目(U1836118) 国家新闻出版署富媒体数字出版内容组织与知识服务重点实验室开放基金项目(ZD2022-10/05) 科技创新2030“新一代人工智能”重大项目(2020AAA0108500)。
关键词 时间关系抽取 自注意力机制 依存句法 局部信息 实体信息 temporal relation extraction self-attention mechanism dependent syntax local information entity information
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