Cross Lingual NER

Cross-lingual Named Entity Recognition (NER) aims to identify and classify named entities (e.g., people, organizations, locations) in text across multiple languages, particularly focusing on low-resource languages lacking extensive annotated data. Current research emphasizes developing robust models, often leveraging pre-trained multilingual language models like XLM-RoBERTa, and employing techniques such as knowledge distillation, data augmentation (including generative methods), and parameter-efficient fine-tuning to improve performance in low-resource scenarios. This field is crucial for advancing multilingual natural language processing and enabling the development of applications that can process and understand information from diverse linguistic sources, impacting fields like finance and healthcare.