What is Named Entity Recognition (NER) Applications and Uses?

NER, short for, Named Entity Recognition is a standard Natural Language Processing problem which deals with information extraction. The primary objective is to locate and classify named entities in text into predefined categories such as the names of persons, organizations, locations, events, expressions of times, quantities, monetary values, percentages, etc. To put it simply, NER deals with extracting the real-world entity from the text such as a person, an organization, or an event. Named Entity Recognition is also simply known as entity identification, entity chunking, and entity extraction. They are quite similar to POS(part-of-speech) tags. NLTK is a standard python library with prebuilt functions and utilities for the ease of use and implementation.