The concept of Named Entity Recognition (NER) was developed by many researchers in the field of Natural Language Processing (NLP) over the years. While it is difficult to attribute it to a single person or group, there have been significant contributions by several researchers.

  One of the earliest works on NER can be traced back to the 1990s, when researchers like Ralph Grishman and Beth Sundheim introduced the idea of identifying and classifying named entities in text. They explored various linguistic patterns, word lists, and rule-based approaches to recognize entities such as person names, organization names, and location names.

  In 2003, Erik F. Tjong Kim Sang and Fien De Meulder released the widely recognized CoNLL-2003 shared task on NER. This task involved identifying and classifying entities in English and German texts. It provided a benchmark dataset and evaluation framework, which played a vital role in promoting research and development of NER systems.

  Subsequently, researchers began exploring machine learning techniques for NER. Many developed models using Conditional Random Fields (CRFs), Hidden Markov Models (HMMs), and various deep learning approaches such as Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and Transformer models.

  Some notable researchers who have contributed significantly to the development of NER include Dan Jurafsky, Christopher Manning, Andrew McCallum, Tomas Mikolov, Yoshua Bengio, and many others.

  Overall, NER is a collective effort of researchers from various institutions and has evolved through numerous publications, shared tasks, and community collaborations in the field of NLP.