Named Entity Recognition (NER) is a subtask of natural language processing that focuses on identifying and classifying key elements in text. These elements, known as named entities, can include names of people, organizations, locations, dates, and other specific items. For example, in the sentence "Apple Inc. was founded by Steve Jobs in Cupertino," NER would recognize "Apple Inc." as an organization, "Steve Jobs" as a person, and "Cupertino" as a location. NER is essential for various applications, such as information retrieval, content classification, and sentiment analysis. By extracting structured information from unstructured text, NER helps improve search engines, chatbots, and data analytics tools. This technology enables better understanding and processing of large volumes of text data, making it easier to derive insights and automate tasks.