In nested Named entity recognition (NER), entities are nested with each
other, and thus requiring more data annotations to address. This leads to the
development of few-shot nested NER, where the prevalence of pretrained language
models with in-context learning (ICL) offers promising solutions. In this work,
we introduce an effective and innovative ICL framework for the setting of
few-shot nested NER. We improve the ICL prompt by devising a novel example
demonstration selection mechanism, EnDe retriever. In EnDe retriever, we employ
contrastive learning to perform three types of representation learning, in
terms of semantic similarity, boundary similarity, and label similarity, to
generate high-quality demonstration examples. Extensive experiments over three
nested NER and four flat NER datasets demonstrate the efficacy of our system.

我们提出了一个有效和创新的 ICL 框架，用于少样本嵌套命名实体识别，通过设计一种新颖的示例演示选择机制 EnDe 检索器，我们通过对比学习进行语义相似性、边界相似性和标签相似性的三类表示学习来生成高质量的演示示例，在三个嵌套 NER 和四个平面 NER 数据集上的大量实验表明我们系统的有效性。