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.

翻译：在嵌套命名实体识别（NER）中，实体相互嵌套，因此需要更多的数据标注来解决这一问题。这催生了少样本嵌套NER的发展，其中具有上下文学习（ICL）能力的预训练语言模型的普及为此提供了有前景的解决方案。本文针对少样本嵌套NER场景，引入了一个有效且创新的ICL框架。我们通过设计一种新颖的示例演示选择机制——EnDe检索器，改进了ICL提示。在EnDe检索器中，我们利用对比学习进行三类表示学习，包括语义相似性、边界相似性和标签相似性，以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证明了我们系统的有效性。