[1]黄汉琴,顾进广,符海东.融合依存句法和实体信息的临床时间关系抽取[J].计算机技术与发展,2024,34(01):128-135.[doi:10. 3969 / j. issn. 1673-629X. 2024. 01. 019]
　HUANG Han-qin,GU Jin-guang,FU Hai-dong.Extraction of Clinical Temporal Relation Fusing Dependency Syntax and Entity Information[J].,2024,34(01):128-135.[doi:10. 3969 / j. issn. 1673-629X. 2024. 01. 019]
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融合依存句法和实体信息的临床时间关系抽取()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
34
期数:
2024年01期
页码:
128-135
栏目:
人工智能
出版日期:
2024-01-10

文章信息/Info

Title:
Extraction of Clinical Temporal Relation Fusing Dependency Syntax and Entity Information
文章编号:
1673-629X(2024)01-0128-08
作者:
黄汉琴1; 2; 3 ; 顾进广1; 2; 3 ; 符海东1; 2; 3
1. 武汉科技大学 计算机科学与技术学院,湖北 武汉 430065;
2. 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,北京 100038;
3. 武汉科技大学 大数据科学与工程研究院,湖北 武汉 430065
Author(s):
HUANG Han-qin1; 2; 3 ; GU Jin-guang1; 2; 3 ; FU Hai-dong1; 2; 3
1. School of Computer Science,Wuhan University of Science and Technology,Wuhan 430065,China;
2. 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;
3. Institute of Big Data Science and Engineering,Wuhan University of Science and Technology,Wuhan 430065,China
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
Keywords:
temporal relation extraction; self-attention mechanism; dependent syntax; local information; entity information
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2024. 01. 019
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。
Abstract:
In clinical texts,temporal relation is crucial to the study of patient’s conditions and treatment options. The current temporalrelation extraction is based on the simple temporal comparison,and only four temporal relations are judged. Considering that there are stilla large number of complex times and relations in Chinese clinical texts,the existing temporal relation extraction task cannot fully expressthe temporal relation of clinical events, referring to the CTO temporal ontology, the extraction task is expanded to complex timerelationship extraction. At the same time,aiming at the semantic complexity of Chinese clinical texts,a model integrating dependencysyntax and entity information is proposed to learn the overall information and entity information of Chinese sentences. The modelscrambles to design dependency feature matrices for intra-sentence temporal relation and inter-sentence temporal relation to guide BERT’sencoder to aggregate global and local information,derive sentence representation vectors on which rich entity information is extractedusing the inner product and Hadamard product. Finally,the sentence information and entity information is imported into the classifier todetermine temporal relation. Compared with baseline model and other deep learning model,the effectiveness of the proposed model isdemonstrated.

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更新日期/Last Update: 2024-01-10