Shot Named Entity Recognition

Shot named entity recognition (NER) focuses on accurately identifying and classifying named entities in text using limited labeled data, a crucial task in low-resource scenarios. Current research emphasizes improving model robustness to noisy data and adversarial attacks, often employing contrastive learning, meta-learning, and techniques that leverage large language models to enhance entity representation and classification. These advancements aim to improve the accuracy and efficiency of NER in various domains, particularly where labeled data is scarce, impacting applications like information extraction and knowledge base construction.