Nested Named Entity Recognition

Nested Named Entity Recognition (NNER) focuses on identifying and classifying entities within text where one entity can be nested inside another, a significant challenge beyond standard named entity recognition. Current research emphasizes developing robust models, including those based on machine reading comprehension, graph neural networks, and sequence-to-sequence architectures, often incorporating techniques like data augmentation and contrastive learning to address data scarcity and improve performance. These advancements are crucial for improving information extraction from complex texts in various domains, such as biomedical literature and news articles, enabling more accurate knowledge graph construction and downstream applications like question answering systems.