无模板提示,通过语义增强的对比学习进行短暂的命名实体识别
IEEE transactions on neural networks and learning systems
|September 26, 2023
概括
本研究引入了一种基于提示符的对比式学习方法,用于短暂的命名实体识别 (NER),避免模板和标签映射. 该方法通过优化提示以语义增强的对比损失来实现最先进的结果.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 快速调整在句子级别的任务中表现出色,但在标记级别的任务中扎,例如命名实体识别 (NER).
- 现有的使用N-gram穿越进行提示的NER方法在计算上昂贵.
- 短暂的学习场景对传统的对比学习方法提出了挑战.
研究的目的:
- 开发一种高效和有效的基于提示的对比式学习方法,用于短暂的NER.
- 消除了模板构建和手动标签词汇映射的需要.
- 为了提高在低资源的NLP任务的对比学习表现.
主要方法:
- 提出了一个无模板提示 (TFP) 方法,使用外部知识来初始化语义.
- 开发了一个语义增强的对比损失,用于提示符和句子嵌入的上下文优化.
- 在没有大量负样本的情况下,在少数镜头设置中启用对比学习.
主要成果:
- 在标签扩展,域调整和低资源泛化任务中实现了最先进的 (SOTA) 性能.
- 在不同环境下,在六个公共数据集中证明了有效性.
- 在NLP任务中成功解决了传统对比学习的局限性.
结论:
- 拟议的基于提示的对比学习方法为短暂的NER提供了显著的进步.
- 该方法是高效的,避免复杂的设置,并实现卓越的结果.
- 这项工作为低资源的NER挑战提供了强有力的解决方案.
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