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

2023-05-20 · 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.

📄 PDF Abstract BibTeX arXiv:2305.12217

Code (1)

zhang-mozhi/promptner 공식 구현 pytorch

Tasks

few-shot-ner Few-shot NERnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)NER Transfer Learning

PromptNER: Prompting For Named Entity Recognition

2023-05-24 · Dhananjay Ashok, Zachary C. Lipton

In a surprising turn, Large Language Models (LLMs) together with a growing arsenal of prompt-based heuristics now offer powerful off-the-shelf approaches providing few-shot solutions to myriad classic NLP problems. Howev…

few-shot-ner Few-shot NER named-entity-recognition Named Entity Recognition+4

PromptNER: Prompt Locating and Typing for Named Entity Recognition

2023-05-26 · Yongliang Shen, Zeqi Tan, Shuhui Wu, Wenqi Zhang 외

Prompt learning is a new paradigm for utilizing pre-trained language models and has achieved great success in many tasks. To adopt prompt learning in the NER task, two kinds of methods have been explored from a pair of s…

Cross-Domain Few-Shot Entity Typing Graph Matching named-entity-recognition+5

Evaluating Named Entity Recognition Using Few-Shot Prompting with Large Language Models

2024-08-28 · Hédi Zeghidi, Ludovic Moncla

This paper evaluates Few-Shot Prompting with Large Language Models for Named Entity Recognition (NER). Traditional NER systems rely on extensive labeled datasets, which are costly and time-consuming to obtain. Few-Shot P…

In-Context Learning named-entity-recognition Named Entity Recognition Named Entity Recognition (NER)+2

Retrieval augmented generation based dynamic prompting for few-shot biomedical named entity recognition using large language models

2025-07-25 · Yao Ge, Sudeshna Das, Yuting Guo, Abeed Sarker arxiv

Biomedical named entity recognition (NER) is a high-utility natural language processing (NLP) task, and large language models (LLMs) show promise particularly in few-shot settings (i.e., limited training data). In this a…

Prompt Engineering

QaNER: Prompting Question Answering Models for Few-shot Named Entity Recognition

2022-03-03 · Andy T. Liu, Wei Xiao, Henghui Zhu, Dejiao Zhang 외

Recently, prompt-based learning for pre-trained language models has succeeded in few-shot Named Entity Recognition (NER) by exploiting prompts as task guidance to increase label efficiency. However, previous prompt-based…

Few-shot NER Named Entity Recognition Named Entity Recognition (NER)Prompt Engineering+1