Margaret Rouse is an award-winning technical writer and teacher known for her ability to explain complex technical subjects simply to a non-technical, business audience. Over…
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.
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Margaret is an award-winning technical writer and teacher known for her ability to explain complex technical subjects to a non-technical business audience. Over the past twenty years, her IT definitions have been published by Que in an encyclopedia of technology terms and cited in articles by the New York Times, Time Magazine, USA Today, ZDNet, PC Magazine, and Discovery Magazine. She joined Techopedia in 2011. Margaret's idea of a fun day is helping IT and business professionals learn to speak each other’s highly specialized languages.
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