Cross Domain NER

Cross-domain named entity recognition (NER) focuses on adapting NER models trained on one data domain to perform effectively on others, addressing the challenge of limited labeled data in specific domains. Current research emphasizes leveraging large language models (LLMs) like ChatGPT and BERT-based architectures, along with techniques like graph matching and label alignment/reassignment, to improve knowledge transfer and handle label inconsistencies across domains. These advancements aim to enhance the robustness and applicability of NER systems in diverse real-world scenarios, such as biomedical text analysis and social media monitoring, where data scarcity is a common hurdle. Improved cross-domain NER capabilities are crucial for building more versatile and reliable natural language processing applications.