what is ner

Named-entity recognition (NER) is a subtask of information extraction in natural language processing (NLP) that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc. . NER involves detecting and categorizing entities from the text and is used in many fields in artificial intelligence (AI), including machine learning (ML), deep learning, and neural networks. NER is a key component of NLP systems, such as chatbots, sentiment analysis tools, and search engines, and is used in healthcare, finance, human resources (HR), customer support, higher education, and social media analysis. The purpose of NER is to identify, categorize, and extract the most important pieces of information from unstructured text without requiring time-consuming human analysis. Its particularly useful for quickly extracting key information from large amounts of data because it automates the extraction process. NER delivers critical insights to organizations about their customers, products, competition, and market trends.