NER Model

Named Entity Recognition (NER) models identify and classify named entities (e.g., people, organizations, locations) within text, a crucial task in natural language processing. Current research emphasizes improving NER performance in specialized domains (e.g., finance, medicine, fashion) and low-resource settings through techniques like transfer learning, graph-based methods, and the adaptation of large language models (LLMs). These advancements are driving improvements in various applications, including information extraction, question answering, and report generation in diverse fields, with a particular focus on mitigating challenges like noisy data and out-of-vocabulary entities.