LoGo-GR:一个局部到全球的图形推理框架,用于从生物医学文献中提取结构化信息
IEEE journal of biomedical and health informatics
|January 24, 2024
概括
这项研究引入了一种用于文档级关系提取的新框架,改进了生物医学文本中复杂相互作用的识别. 局部到全球图形推理 (LoGo-GR) 方法增强了从大型科学文献数据集的知识发现.
科学领域:
- 计算生物学 计算生物学
- 自然语言处理自然语言处理.
- 生物信息学是一种生物信息学.
背景情况:
- 生物医学文献包含复杂的实体相互作用,分布在多个句子中.
- 手动提取生物医学知识是昂贵和不切实际的,因为越来越多的文献.
- 文档级关系提取对于有效地从庞大的医学文本中挖掘结构化知识至关重要.
研究的目的:
- 为了应对在文档级关系提取中综合全球上下文和本地依赖性的挑战.
- 提出一个新的框架,局部到全球图形推理 (LoGo-GR),用于捕捉复杂实体交互.
- 从非结构化文本中提取结构化生物医学知识的准确性和效率.
主要方法:
- 开发了一个局部到全球图形推理 (LoGo-GR) 框架,利用一种新的偏差图注意力机制 (B-GAT).
- LoGo-GR学习全球上下文特征和局部关系路径依赖性,使用提及级交互和实体级路径图.
- B-GAT将结构依赖作为注意偏差集成到图表注意力机制 (GAT) 中,以指导信息聚合.
主要成果:
- 在药物突变相互作用 (DV) 数据集 (96.14%-97.39% F1) 上实现了最先进的性能.
- 在化学诱导疾病 (CDR) 数据集 (68.89% F1) 和基因疾病协会 (GDA) 数据集 (84.22% F1) 上表现出先进的性能.
- 在通用域级文档级关系提取数据集DocRED上展示了强大的性能.
结论:
- LoGo-GR框架有效地捕捉了在文档级生物医学文本中实体之间的复杂相互作用.
- 拟议的B-GAT机制通过自适应地指导信息聚合来增强图形推理.
- LoGo-GR是一个有效和强大的框架,用于文档级别的关系提取,适用于生物医学和一般领域.
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