Named Entity

Named entity recognition (NER) focuses on automatically identifying and classifying named entities (e.g., people, organizations, locations) within text, aiming to improve information extraction and knowledge representation. Current research emphasizes enhancing NER performance using advanced deep learning models like BERT and LLMs, often incorporating techniques such as attention mechanisms, contrastive learning, and data augmentation to address challenges like imbalanced datasets and complex entity structures. These advancements have significant implications for various applications, including clinical coding, financial analysis, and historical text processing, by enabling more efficient and accurate information extraction from large text corpora.