PromptNER: A Prompting Method for Few-shot Named Entity Recognition via k Nearest Neighbor Search

• URL: http://arxiv.org/abs/2305.12217v1
• Date: Sat, 20 May 2023 15:47:59 GMT
• Title: PromptNER: A Prompting Method for Few-shot Named Entity Recognition via k Nearest Neighbor Search
• Authors: Mozhi Zhang, Hang Yan, Yaqian Zhou, Xipeng Qiu
• Abstract summary: 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.
• Score: 56.81939214465558
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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