Named Entity Recognition Task

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A named entity recognition task is an entity mention recognition task that is restricted to the detection and classification (recognition) of named entity mentions and their entity class.



References

2014

  • (Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/Named-entity_recognition Retrieved:2014-2-17.
    • 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 that highlights where the named entities are, such as this one:

       :<ENAMEX TYPE="PERSON">Jim</ENAMEX>bought<NUMEX TYPE="QUANTITY">300</NUMEX>shares of<ENAMEX TYPE="ORGANIZATION">Acme Corp.</ENAMEX> in <TIMEX TYPE="DATE">2006</TIMEX>.

      In this example, the annotations are marked using XML ENAMEX elements, following the format 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%. [1] [2]

  1. Elaine Marsh, Dennis Perzanowski, "MUC-7 Evaluation of IE Technology: Overview of Results", 29 April 1998 PDF
  2. MUC-07 Proceedings (Named Entity Tasks)

2011

2010

2009

2008

2008

2007

2005

2004

2003

  • (Grishman, 2003) ⇒ Ralph Grishman. (2003). “Information Extraction.” In: * (Mitkov, 2003).
    • QUOTE: In conventional treatments of language structure, little attention is paid to proper names, addresses, quantity phrases, etc. Presentations of language analysis typically begin by looking words up in a dictionary and identifying them as noun, verbs, adjectives, etc. In fact, however, most tests include lots of names, and if a system cannot identify these as linguistic units (and, for most tasks, identify their type), it will be hard pressed to produce a linguistic analysis of the text.

2002

1999

1996

1980