• DocumentCode
    2915359
  • Title

    Combining Syntactic Structured and Flat Features for Relation Extraction Using Co-training

  • Author

    Qiu, Jing ; Liao, Lejian ; Li, Peng

  • Author_Institution
    Beijing Lab. of Intell. Inf. Technol., Beijing Inst. of Technol., Beijing, China
  • fYear
    2009
  • fDate
    4-6 Nov. 2009
  • Firstpage
    401
  • Lastpage
    404
  • Abstract
    A parse tree contains rich syntactic structured information, and the structured features have been proved effective in relation extraction. In this paper, we proposed another way to efficiently utilize structured features but in a weakly learning way. Co-training algorithm was chosen by us, the structured features were set to be one view of it, and the flat features were set to be the other. Through using co-training algorithm, we can combine both flat and structured information for relation extraction.
  • Keywords
    feature extraction; learning (artificial intelligence); text analysis; co-training algorithm; flat information; parse tree; relation extraction; syntactic structured information; Convolution; Data mining; Event detection; Feature extraction; Intelligent networks; Intelligent structures; International collaboration; Kernel; Laboratories; Support vector machines; cotraining; kernel method; relation extraction;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Networking and Collaborative Systems, 2009. INCOS '09. International Conference on
  • Conference_Location
    Barcelona
  • Print_ISBN
    978-1-4244-5165-4
  • Electronic_ISBN
    978-0-7695-3858-7
  • Type

    conf

  • DOI
    10.1109/INCOS.2009.15
  • Filename
    5369323