• DocumentCode
    168416
  • Title

    REEL: A Relation Extraction Learning framework

  • Author

    Barrio, P. ; Simoes, G. ; Galhardas, H. ; Gravano, L.

  • Author_Institution
    Columbia Univ., New York, NY, USA
  • fYear
    2014
  • fDate
    8-12 Sept. 2014
  • Firstpage
    455
  • Lastpage
    456
  • Abstract
    We introduce the REEL (RElation Extraction Learning) framework, an open source framework that facilitates the development and evaluation of relation extraction systems over text collections. To define a relation extraction system for a new relation and text collection, users only need to specify the parsers to load the collection, the relation and its constraints, and the learning and extraction techniques to be used. This makes REEL a powerful framework to enable the deployment and evaluation of relation extraction systems for both application building and research.
  • Keywords
    learning (artificial intelligence); public domain software; text analysis; word processing; REEL; extraction technique; learning technique; open source; relation extraction learning; relation extraction systems; text collections; Data mining; Feature extraction; Loading; Logic gates; Natural language processing; Text processing; Training;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Digital Libraries (JCDL), 2014 IEEE/ACM Joint Conference on
  • Conference_Location
    London
  • Type

    conf

  • DOI
    10.1109/JCDL.2014.6970222
  • Filename
    6970222