通过层次树图和关系细分模块提取文档级生物医学关系
Jianyuan Yuan1, Fengyu Zhang1, Yimeng Qiu1
1School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China.
Bioinformatics (Oxford, England)
|June 25, 2024
概括
这项研究介绍了HTGRS,这是从文档中提取生物医学关系的新框架. 它通过考虑实体对信息和使用层次树图和关系分割来提高准确性.
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:
- 在文档层面 (Bio-DocRE) 提取生物医学关系对于理解复杂的生物文本至关重要.
- 当前的方法往往忽略了实体对信息在关系预测中的重要性.
- 现有的方法主要依赖于图形或变压器,直接建模实体特征.
研究的目的:
- 提出一个创新的框架,HTGRS,以增强Bio-DocRE.
- 通过将实体对信息作为中间状态来改进关系预测.
- 为了更好地捕获信息,将Bio-DocRE任务解为一个三阶段的过程.
主要方法:
- 开发了层次树图 (HTG),以整合基于实体的关系推理的文档信息.
- 概念化Bio-DocRE作为一个表格填充问题,灵感来自语义细分.
- 引入了一个关系分割 (RS) 模块,以使用实体对信息来完善关系推理.
主要成果:
- 与最先进的方法相比,提出的HTGRS框架显示出更高的性能.
- 在三个基准数据集上进行了广泛的实验,验证了该方法的有效性.
- 该框架在生物医学关系提取准确度方面取得了显著的改进.
结论:
- 该HTGRS框架为Bio-DocRE提供了一种新有效的方法.
- 整合实体对信息和层次图形结构可以增强关系预测.
- 拟议的方法推进了自动化生物医学知识发现领域.
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