Back التعرف على الكيانات المسماة Arabic Reconeixement d'entitats nomenades Catalan Rozpoznávání pojmenovaných entit Czech Named-entity recognition German Reconocimiento de entidades nombradas Spanish Entitateen izenen ezagutzea Basque Reconnaissance d'entités nommées French Aithint eintiteas ainmnithe Irish זיהוי ישויות HE 固有表現抽出 Japanese
Named-entity recognition (NER) (also known as (named) entity identification, entity chunking, and entity extraction) is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.
Most research on NER/NEE 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 that highlights the names of entities:
[Jim]Person bought 300 shares of [Acme Corp.]Organization in [2006]Time.
In this example, a person name consisting of one token, a two-token company name and a temporal expression have been detected and classified.
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%.[1][2]
- ^ Elaine Marsh, Dennis Perzanowski, "MUC-7 Evaluation of IE Technology: Overview of Results", 29 April 1998 PDF
- ^ MUC-07 Proceedings (Named Entity Tasks)