Named Entity Recognition (NER) is a subtask of information extraction in Natural Language Processing (NLP) that involves identifying and classifying named entities in text into predefined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, and more. NER is usually approached as a sequence labeling problem, where the model detects a word or string of words as an entity and classifies it into a predefined category. The process of NER involves detecting and categorizing important information in text known as named entities. Some of the categories that are the most important architecture in NER include person, organization, and place/location.

NER can be performed using various methods such as dictionary-based systems, unsupervised machine learning systems, and deep learning. The first step in NER is to acquire and process the data, which can either be labeled data or built from scratch based on the use case. The second step is to prepare the input and fine-tune the model, which involves taking care of sensitivity, special characters, and spacing of words to improve accuracy and make the model more generic for other datasets.

Named Entity Recognition provides a range of advantages when used appropriately, such as automating the information extraction of large amounts of data and analyzing key information. However, the problem of named-entity recognition is far from being solved, and the main efforts are directed towards reducing the annotations labor by employing semi-supervised learning, robust performance across domains, and scaling up to fine-grained entity types.