Named entity recognition (NER) is a process in natural language processing (NLP) that involves identifying and extracting named entities from text data. Named entities are specific words or phrases that refer to real-world objects, such as people, organizations, locations, and so on.
NER algorithms are trained on large datasets of annotated text, where named entities have been identified and labeled. The algorithms then use this training data to learn the characteristics and features of named entities, and to identify and extract named entities from new text.
There are different approaches to NER, including rule-based approaches, which use a set of predefined rules to identify named entities; and machine learning-based approaches, which use statistical models and algorithms to learn from labeled data and make predictions about the named entities in new text.
Overall, named entity recognition is an important tool in natural language processing, and it is a widely used technique for identifying and extracting named entities from text data. It can be a valuable resource for businesses, researchers, and other organizations looking to extract and analyze named entities from text data for a variety of purposes.