实体关系提取的denoising模型的研究,使用与远程监督的层次对比学习来进行实体关系提取
Ayiguli Halike1,2, Aishan Wumaier3,4, Kahaerjiang Abiderexiti3,4
1Institute of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830017, China. aygul1212@stu.xju.edu.cn.
Scientific reports
|July 2, 2025
概括
这项研究引入了一种新的等级对比学习框架,以减少远程监督中杂的标签. 该方法提高了准确性,并改善了罕见关系的识别,为少数民族语言处理提供了新的方法.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 计算语言学 计算语言学
背景情况:
- 远程监控使用知识库自动标记文本数据,但经常引入噪音标签.
- 关系提取中的噪音标签会降低准确性,并阻碍识别不太常见的 (长尾) 关系.
研究的目的:
- 为维吾尔族的关系提取提出一个层次化的对比学习框架,以减轻在远程监督中的噪音标签.
- 提高模型识别常见关系和长尾关系的能力.
主要方法:
- 一个三层层的等级学习架构,整合了全球和本地信息.
- 多头自我注意力用于无声化,上下文意识的表示 (多粒度重新上下文化).
- 动态梯度对抗性扰动用于数据增强和伪阳性样本生成.
主要成果:
- 对维吾尔关系提取的准确性和稳定性的显著改进.
- 与现有方法相比,在识别罕见关系方面提高了能力.
- 在处理噪音标签方面验证框架的有效性和创新性.
结论:
- 拟议的框架有效地减少了维吾尔族关系提取的远程监控中的噪音.
- 它为改善关系提取性能提供了一种新的方法,特别是对于少数民族语言.
- 这项研究为远程监督和关系提取领域提供了新的方法.
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