Abstract
Named Entity Recognition (NER) is crucial for extracting structured information from text. While traditional methods rely on rules, Conditional Random Fields (CRFs), or deep learning, the advent of large-scale Pre-trained Language Models (PLMs) offers new possibilities. PLMs excel at contextual learning, potentially simplifying many natural language processing tasks. However, their application to NER remains underexplored. This paper investigates leveraging the GPT3 PLM for NER without fine-tuning. We propose a novel scheme that utilizes carefully crafted templates and context examples selected based on semantic similarity. Our experimental results demonstrate the feasibility of this approach, suggesting a promising direction for harnessing PLMs in NER.
