• DocumentCode
    741421
  • Title

    Information Extraction

  • Author

    Grishman, Ralph

  • Author_Institution
    New York University
  • Volume
    30
  • Issue
    5
  • fYear
    2015
  • Firstpage
    8
  • Lastpage
    15
  • Abstract
    Much of the world´s knowledge is recorded in natural language text, but making effective use of it in this form poses a major challenge. Information extraction converts this knowledge to a structured form suitable for computer manipulation, opening up many possibilities for using it. In this review, the author describes the processing pipeline of information extraction, how the pipeline components are trained, and how this training can be made more efficient. He also describes some of the challenges that must be addressed for information extraction to become a more widely used technology.
  • Keywords
    Chemistry; Databases; Hidden Markov models; Semantics; Syntactics; Tagging; Training; NLP; information extraction; intelligent systems; natural language processing;
  • fLanguage
    English
  • Journal_Title
    Intelligent Systems, IEEE
  • Publisher
    ieee
  • ISSN
    1541-1672
  • Type

    jour

  • DOI
    10.1109/MIS.2015.68
  • Filename
    7243219