Getting to Know Named Entity Recognition: Better Information Retrieval

Abstract

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.

Keywords:

• Artificial intelligence (AI)
• information-extraction techniques
• named entity recognition (NER)
• natural language processing (NLP)
• structured data

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.

Notes on contributors

Borui Zhang

Borui Zhang, PhD ([email protected]) is the Natural Language Processing Specialist in the Academic Research Consulting and Services Department at the George A. Smathers Libraries at the University of Florida, Gainesville, Florida, USA.