Few-shot Named Entity Recognition (NER) aims to extract named entities using
only a limited number of labeled examples. Existing contrastive learning
methods often suffer from insufficient distinguishability in context vector
representation because they either solely rely on label semantics or completely
disregard them. To tackle this issue, we propose a unified label-aware
token-level contrastive learning framework. Our approach enriches the context
by utilizing label semantics as suffix prompts. Additionally, it simultaneously
optimizes context-context and context-label contrastive learning objectives to
enhance generalized discriminative contextual representations.Extensive
experiments on various traditional test domains (OntoNotes, CoNLL'03, WNUT'17,
GUM, I2B2) and the large-scale few-shot NER dataset (FEWNERD) demonstrate the
effectiveness of our approach. It outperforms prior state-of-the-art models by
a significant margin, achieving an average absolute gain of 7% in micro F1
scores across most scenarios. Further analysis reveals that our model benefits
from its powerful transfer capability and improved contextual representations.

我们提出了统一的标签感知令牌级对比学习框架，通过利用标签语义作为后缀提示来丰富上下文，同时优化上下文 - 上下文和上下文 - 标签对比学习目标，从而提高推广辨别性的上下文表示。广泛的实验表明我们的方法在各种传统测试领域和大规模少样本 NER 数据集上优于先前的最先进模型，微粒 F1 分数平均绝对增益达到 7%。进一步的分析揭示了我们模型受益于其强大的迁移能力和改进的上下文表示。