增强知识和充分利用文档信息,以提取文档级生物医学关系.
Xin Chen1, Liang He2,3, Yuhan Liu1
1School of Computer Science and Technology, Xinjiang University, Urumqi, 830017, China.
Scientific reports
|December 20, 2025
概括
本研究介绍了KnowFDI,这是一个用于文档级生物医学关系提取的新框架. 通过整合本地/全球背景,文档结构和外部知识来提高KnowFDI的准确性,以便更好地发现生物医学知识.
科学领域:
- 生物医学信息学 生物医学信息学
- 自然语言处理自然语言处理.
- 计算生物学 计算生物学
背景情况:
- 文档级生物医学关系提取 (BioDocuRE) 对于发现跨越多个句子的关系至关重要.
- 现有的方法在整合外部知识和利用复杂关系的文档结构方面扎.
研究的目的:
- 引入KnowFDI,这是一个新的框架,用于文档级BioDocuRE.
- 为了提高复杂的,远程的生物医学关系的提取精度.
主要方法:
- 知识FDI结合了当地/全球背景,文档结构 (通过桥接节点的句子间关系) 和以外部实体为中心的知识.
- 使用预先训练的语言模型进行编码和多视图表示学习,并与道智能的注意力融合.
- 结合了来自外部生物医学知识库的描述性知识,以丰富实体语义.
主要成果:
- 在CDR和GDA基准指标上,KnowFDI取得了最先进的表现.
- 在整体和句子间关系提取方面显著改进.
- 在识别复杂的,远程的生物医学关系方面优于以前的方法.
结论:
- KnowFDI的层次信息融合和外部知识集成对于破译复杂的文档级依赖关系至关重要.
- 该框架通过改进的关系提取有效地增强生物医学知识的发现.
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