一个联合实体 关系 文件层次的提取方法 传统中医文本
Wenxuan Xu1, Lin Wang1, Mingchuan Zhang1
1School of Information Engineering, Henan University of Science and Technology, Luoyang, 471023, China.
Artificial intelligence in medicine
|June 27, 2024
概括
本研究介绍了使用图形卷积网络进行传统中医 (TCM) 实体关系挖掘的新框架. 该方法显著改善了从TCM文本中提取诊断和治疗信息的方法.
科学领域:
- 计算语言学计算语言学
- 生物信息学是一种生物信息学.
- 传统中国医药 (TCM) 的研究.
背景情况:
- 传统中医 (TCM) 拥有复杂的理论框架,对疾病的诊断和治疗有价值.
- 现有的TCM文本数据是无组织的,缺乏标准化,妨碍知识提取.
- 准确的实体和关系提取对于利用TCM知识至关重要.
研究的目的:
- 建议从文档级的TCM文本中为采矿实体和关系提供一个共同的提取框架.
- 通过微调预先训练的语言模型与TCM领域知识来开发特定任务的模型.
- 加强从非结构化的TCM病例研究中提取诊断和治疗信息.
主要方法:
- 一个使用图形卷积网络 (GCNs) 进行实体关系挖掘的联合提取框架.
- 微调预先训练的语言模型与TCM领域知识,以创建一个专门的模型.
- 采用多关系GCN,词汇融合编码和TCM词典信息来进行全面的实体和关系提取.
主要成果:
- 拟议的方法在TCM数据集上实现了命名实体识别 (NER) 的F1得分90.7%.
- 该系统在TCM文本中获得了76.14%的F1得分,用于关系提取 (RE).
- 这种方法显著优于TCM实体关系挖掘中现有的最先进的方法.
结论:
- 开发的框架有效地从文档级TCM文本中提取实体和关系.
- 这一进步增强了为TCM诊断和治疗挖掘宝贵知识的能力.
- 该方法为处理和理解TCM文本数据提供了一种标准化的方法.
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