PromptNER: A Prompting Method for Few-shot Named Entity Recognition via
  k Nearest Neighbor Search
Mozhi Zhang Hang Yan Yaqian Zhou Xipeng Qiu
Few-shot Named Entity Recognition (NER) is a task aiming to identify named
entities via limited annotated samples. Recently, prototypical networks have
shown promising performance in few-shot NER. Most of prototypical networks will
utilize the entities from the support set to construct label prototypes and use
the query set to compute span-level similarities and optimize these label
prototype representations. However, these methods are usually unsuitable for
fine-tuning in the target domain, where only the support set is available. In
this paper, we propose PromptNER: a novel prompting method for few-shot NER via
k nearest neighbor search. We use prompts that contains entity category
information to construct label prototypes, which enables our model to fine-tune
with only the support set. Our approach achieves excellent transfer learning
ability, and extensive experiments on the Few-NERD and CrossNER datasets
demonstrate that our model achieves superior performance over state-of-the-art
methods.