• DocumentCode
    555176
  • Title

    Minority opinions abstraction

  • Author

    Tan Xian ; Li Fang

  • Author_Institution
    Hubei Inst. for Nat., Enshi, China
  • Volume
    1
  • fYear
    2011
  • fDate
    20-22 Aug. 2011
  • Firstpage
    430
  • Lastpage
    434
  • Abstract
    To deal with the multi-granularity web documents, we propose EMTF (EM Topic Fusion) algorithm to rank the by-joint possibility, resulting of their relevance to be the topic and their granularity. We first extract features from the different granularity web documents to establish the quantitative relationship amongst them. Then, the process of multi-granularity web documents analysis that leads to heretofore unknown information and opinions that valuable potential, minority and contentious respectively, which integrates the time, content, reprint and link information. Experiments show that EMTF achieves the best overall performance with high effectiveness and robustness.
  • Keywords
    Internet; abstracting; document handling; feature extraction; EM topic fusion algorithm; EMTF; by-joint possibility; feature extraction; minority opinions abstraction; multigranularity Web documents analysis; quantitative relationship; Artificial neural networks; Computational modeling; Electronic learning; Feature extraction; Internet; Ontologies; Semantics; Topic Fusion; minority and contentious; multi-granularity; potential; unknown information and opinions;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Technology and Artificial Intelligence Conference (ITAIC), 2011 6th IEEE Joint International
  • Conference_Location
    Chongqing
  • Print_ISBN
    978-1-4244-8622-9
  • Type

    conf

  • DOI
    10.1109/ITAIC.2011.6030239
  • Filename
    6030239