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    <abstract>“电子病历是医疗信息的重要来源,包含大量与医疗相关的领域知识。本文从糖尿病电子病历文本入手,在调研了国内外已有的电子病历语料库的基础上,参考坉圲坂圲实体及关系分类,建立了糖尿病电子病历实体及实体关系分类体系,并制定了标注规范。利用实体及关系标注平台,进行了实体及关系预标注及多轮人工校对工作,形成了糖尿病电子病历实体及关系标注语料库(Diabetes Electronic Medical Record entity and Related Corpus DEMRC)。所构建的DEMRC包含8899个实体、456个实体修饰及16564个关系。对DEMRC进行一致性评价和分析,标注结果达到了较高的一致性。针对实体识别和实体关系抽取任务,分别采用基于迁移学习的Bi-LSTM-CRF模型和RoBERTa模型进行初步实验,并对语料库中的各类实体及关系进行评估,为后续糖尿病电子病历实体识别及关系抽取研究以及糖尿病知识图谱构建打下基础。”</abstract>
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%0 Conference Proceedings
%T 糖尿病电子病历实体及关系标注语料库构建(Construction of Corpus for Entity and Relation Annotation of Diabetes Electronic Medical Records)
%A Ye, Yajuan
%A Hu, Bin
%A Zhang, Kunli
%A Zan, Hongying
%S Proceedings of the 20th Chinese National Conference on Computational Linguistics
%D 2021
%8 aug
%I Chinese Information Processing Society of China
%C Huhhot, China
%G Chinese
%F ye-etal-2021-tang
%X “电子病历是医疗信息的重要来源,包含大量与医疗相关的领域知识。本文从糖尿病电子病历文本入手,在调研了国内外已有的电子病历语料库的基础上,参考坉圲坂圲实体及关系分类,建立了糖尿病电子病历实体及实体关系分类体系,并制定了标注规范。利用实体及关系标注平台,进行了实体及关系预标注及多轮人工校对工作,形成了糖尿病电子病历实体及关系标注语料库(Diabetes Electronic Medical Record entity and Related Corpus DEMRC)。所构建的DEMRC包含8899个实体、456个实体修饰及16564个关系。对DEMRC进行一致性评价和分析,标注结果达到了较高的一致性。针对实体识别和实体关系抽取任务,分别采用基于迁移学习的Bi-LSTM-CRF模型和RoBERTa模型进行初步实验,并对语料库中的各类实体及关系进行评估,为后续糖尿病电子病历实体识别及关系抽取研究以及糖尿病知识图谱构建打下基础。”
%U https://aclanthology.org/2021.ccl-1.56
%P 622-632

Markdown (Informal)

[糖尿病电子病历实体及关系标注语料库构建(Construction of Corpus for Entity and Relation Annotation of Diabetes Electronic Medical Records)](https://aclanthology.org/2021.ccl-1.56) (Ye et al., CCL 2021)

• 糖尿病电子病历实体及关系标注语料库构建(Construction of Corpus for Entity and Relation Annotation of Diabetes Electronic Medical Records) (Ye et al., CCL 2021)

ACL

• Yajuan Ye, Bin Hu, Kunli Zhang, and Hongying Zan. 2021. 糖尿病电子病历实体及关系标注语料库构建(Construction of Corpus for Entity and Relation Annotation of Diabetes Electronic Medical Records). In Proceedings of the 20th Chinese National Conference on Computational Linguistics, pages 622–632, Huhhot, China. Chinese Information Processing Society of China.