George A. Smathers Libraries at the University of Florida, Gainesville, Florida, USA.
Med Ref Serv Q. 2024 Apr-Jun;43(2):196-202. doi: 10.1080/02763869.2024.2335139. Epub 2024 May 9.
Named entity recognition (NER) is a powerful computer system that utilizes various computing strategies to extract information from raw text input, since the early 1990s. With rapid advancement in AI and computing, NER models have gained significant attention and been serving as foundational tools across numerus professional domains to organize unstructured data for research and practical applications. This is particularly evident in the medical and healthcare fields, where NER models are essential in efficiently extract critical information from complex documents that are challenging for manual review. Despite its successes, NER present limitations in fully comprehending natural language nuances. However, the development of more advanced and user-friendly models promises to improve work experiences of professional users significantly.
命名实体识别(NER)是一种强大的计算机系统,自 20 世纪 90 年代初以来,它利用各种计算策略从原始文本输入中提取信息。随着人工智能和计算技术的飞速发展,NER 模型受到了广泛关注,并成为许多专业领域的基础工具,用于组织研究和实际应用中的非结构化数据。这在医疗和保健领域尤为明显,NER 模型在从复杂文档中高效提取关键信息方面发挥着重要作用,这些文档对于人工审查来说具有挑战性。尽管取得了成功,但 NER 在完全理解自然语言细微差别方面存在局限性。然而,更先进和用户友好的模型的开发有望显著改善专业用户的工作体验。