Named Entity Recognition Reinforced by Prompt Learning

In recent years, Named Entity Recognition (NER), as a fundamental task in information extraction, has found wide applications across various domains, yet continues to face significant methodological challenges. Traditional Named Entity Recognition (NER) methods often lack universal applicability and require model retraining for each new domain, despite being a fundamental task in information extraction with wide applications across various domains. The emergence of Large Language Models (LLMs) has brought new possibilities to NER tasks, offering unprecedented flexibility and promising results through their powerful language understanding capabilities. However, even these advanced models face notable challenges: they often struggle with precise entity boundary recognition and suffer from the “hallucination” problem, generating false or inconsistent entity information. Despite LLMs' strong general language understanding abilities, the complexity of cross-domain knowledge transfer and the requirement for substantial labeled training data continue to pose significant challenges, especially in specialized domains. While recent research has explored various ways to leverage LLMs' capabilities through hybrid approaches that combine their strengths with traditional methods, achieving robust and reliable entity recognition across diverse domains remains a substantial challenge in the field.