PromptNER: A Prompting Method for Few-shot Named Entity Recognition via k Nearest Neighbor Search
This addresses the challenge of fine-tuning NER models in target domains with limited annotated data, representing an incremental improvement over existing prototypical networks.
The paper tackles the problem of few-shot named entity recognition (NER) by proposing PromptNER, a prompting method using k nearest neighbor search, which achieves superior performance over state-of-the-art methods on Few-NERD and CrossNER datasets.
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.