What is named entity recognition (NER)?

Named entity recognition (NER) is a crucial subtask within the field of natural language processing (NLP) focused on identifying and classifying named entities in text into predefined categories such as names of people, organizations, locations, dates, and more. This process involves analyzing unstructured text data to extract information that can be used for various applications, such as information retrieval, question answering, and knowledge graph construction.

Understanding named entities is vital for applications that require comprehension of key entities being discussed within a text, allowing for a more refined and structured understanding of the content. By identifying these entities, NER systems can assist with tasks like sentiment analysis, content categorization, and improving search capabilities by linking entities with relevant information.

This makes the choice that describes NER as a subtask of NLP that classifies named entities in text the correct answer, highlighting its fundamental role in automating the extraction of important information from large volumes of textual data.