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:
- Jim bought 300 shares of Acme Corp. in 2006.
And producing an annotated block of text, such as this one:
Jim bought300 shares ofAcme Corp. in2006 .
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 for English 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 algorithms had roughly twice the error rate (6.61%) of human annotators (2.40% and 3.05%).
Read more about Named-entity Recognition: Approaches, Problem Domains, Named Entity Types, Current Challenges and Research, Available Technology, NER Evaluation Forums
Famous quotes containing the word recognition:
“I waited and worked, and watched the inferior exalted for nearly thirty years; and when recognition came at last, it was too late to alter events, or to make a difference in living.”
—Ellen Glasgow (1873–1945)