Low Resource Named Entity Recognition

Low-resource named entity recognition (NER) focuses on accurately identifying and classifying named entities (e.g., people, organizations) in text when limited labeled training data is available. Current research emphasizes techniques like transfer learning from high-resource languages, data augmentation strategies (including prompt-based methods), and alternative loss functions such as AUC maximization to address data imbalance. These advancements aim to improve NER performance for under-resourced languages and domains, impacting applications ranging from information extraction in diverse languages to building more inclusive and effective NLP systems.