• DocumentCode
    2539102
  • Title

    Feature Terms Analyzing Strategy for Recruiting Websites

  • Author

    Hong, Xu ; Zhang, YongJun ; Jiong, Zhang

  • Author_Institution
    Sch. of Inf. Technol., Shandong Inst. of Commerce & Technol., Jinan, China
  • fYear
    2012
  • fDate
    12-14 Oct. 2012
  • Firstpage
    451
  • Lastpage
    453
  • Abstract
    As we know text information on web page has grown exponentially. It is a hot research area in data processing by reasonably extracting and analysis for unstructured information, so as to mine novel, latent useful pattern. Focusing on imprecise classified text set about job hunting web site, discovering topic relevant feature terms is an effective way to find new tendency for work ability demanding. In this paper, we propose a job relevant feature extracting method better than methods of TF-IDF, maximum entropy and lexical chain to reflect the demanding of tendency, and prove that it is effective by contrast testing.
  • Keywords
    Web sites; classification; feature extraction; information retrieval; text analysis; Web page; classified text set; data processing; feature terms analyzing strategy; job relevant feature extracting method; latent useful pattern; text information; unstructured information analysis; unstructured information extraction; Agricultural products; Business; Educational institutions; Entropy; Feature extraction; Filtering algorithms; Information technology; concurrent terms; feature term extraction; maximum relevance;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Business Computing and Global Informatization (BCGIN), 2012 Second International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4673-4469-2
  • Type

    conf

  • DOI
    10.1109/BCGIN.2012.123
  • Filename
    6382564