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.
@article{arxiv.2305.12217,
title = {PromptNER: A Prompting Method for Few-shot Named Entity Recognition via k Nearest Neighbor Search},
author = {Mozhi Zhang and Hang Yan and Yaqian Zhou and Xipeng Qiu},
journal= {arXiv preprint arXiv:2305.12217},
year = {2023}
}