句子级别关系 语义 通过对比句子学习
IEEE transactions on pattern analysis and machine intelligence
|September 9, 2025
概括
本研究引入了一种新的关系三重提取 (RTE) 方法,该方法考虑了句子语义和样本间的依赖关系. 通过结合关系信息和使用监督对比学习,该方法可以增强语义理解,提高提取精度.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 句子层面的语义对于语言理解至关重要.
- 现有的关系三重提取 (RTE) 模型往往忽略了句子语义和样本间的依赖关系.
- 这导致关系无意识的语义和个别训练的样本,阻碍了性能.
研究的目的:
- 为了解决当前RTE模型中的局限性.
- 将关系信息纳入用于关系意识语义的文本编码中.
- 为了利用样本间的依赖关系来学习歧视性语义.
主要方法:
- 提出了一个模型不可知的多关系检测任务.
- 开发了模型无意识的多关系监督的对比学习.
- 设计的反向标签频率权重和层次标签嵌入机制.
主要成果:
- 提出的方法显著和持续地改善了各种脊柱的性能.
- 对四个公共基准进行了实验.
- 模型分析证实了拟议技术的有效性.
结论:
- 这种新的方法通过整合关系意识语义和样本间依赖关系来增强RTE模型.
- 该方法提供了一种灵活而有效的方法来改进关系式三重提取.
- 这些技术解决了诸如标签不平衡和关系层次等挑战.
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