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Named-entity recognition (NER) refers to a data extraction task that is responsible for finding, storing and sorting textual content into default categories such as the names of persons, organizations, locations, expressions of times, quantities, monetary values and percentages. Duties of NER includes extraction of data directly from plain English text sentences.
Named-entity recognition is also known as entity identification, entity chunking and entity extraction.
Named-entity recognition is a state-of-the-art intelligence system that works with nearly the efficiency of a human brain. The system is structured in such a way that it is capable of finding entity elements from raw data and can determine the category in which the element belongs. The system reads the sentence and highlights the important entity elements in the text. NER might be given separate sensitive entities depending on the project. This means that the NER system designed for one project may not be reused for another task. Similarly, NER faces many challenges which include the extraction of correct information for specific but closely related categories.