Named Entity Recognition (NER) is a field in Natural Language Processing (NLP) that aims to identify and classify named entities in text into predefined categories such as person names, locations, organizations, dates, etc. The concept of NER has been around for several decades, and its development can be traced back to the early 1990s.

  One of the earliest works in NER can be attributed to the research paper titled "Named Entity Recognition and the CoNLL-2003 Shared Task" by Tjong Kim Sang and Erik F. Tjong Kim Sang, published in 2003. This paper introduced the CoNLL-2003 Shared Task, which aimed to promote and evaluate the development of NER systems.

  However, it's important to note that the idea of identifying named entities and their roles in text can be found in older works as well. For example, the concept of "information extraction," which includes NER as a subtask, was introduced in the 1990s.

  Over the years, NER has gained significant attention and witnessed advancements in the form of different approaches, algorithms, and datasets for training and evaluation. Researchers and practitioners continue to work on improving the accuracy and efficiency of NER models, making it an ongoing and active area of research in NLP.