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Named entity recognition (NER) (also known as entity identification and entity extraction) is a...

Named entity recognition (NER) (also known as entity identification and entity extraction) is a subtask of information extraction that seeks to locate and classify atomic elements in text into predefined categories such as the names of persons, organizations, locations, expressions of times, quantities, monetary values, percentages, etc. Most research on NER systems has been structured as taking an unannotated block of text, such as this one: And producing an annotated block of text, such as this one: In this example, the annotations have been done using so-called ENAMEX tags that were developed for the Message Understanding Conference in the 1990s. State-of-the-art NER systems produce near-human performance. For example, the best system entering MUC-7 scored 93.39% of f-measure while human annotators scored 97.60% and 96.95%. These results indicate the algorithms had roughly twice the error rate (6.61%) as human annotators (2.40% and 3.05%). NER systems have been created that use linguistic grammar-based techniques as well as statistical models. Hand-crafted grammar-based systems typically obtain better results, but at the cost of months of work by experienced computational