• DocumentCode
    476271
  • Title

    Analysis of cognition commonality using descriptive corpus

  • Author

    Wang, Hsien-chang ; Li, Cheng-chieh ; Yang, Pei-ching

  • Author_Institution
    Dept. of Inf. Manage., Chang Jung Christian Univ., Tainan
  • Volume
    6
  • fYear
    2008
  • fDate
    12-15 July 2008
  • Firstpage
    3178
  • Lastpage
    3182
  • Abstract
    This paper aims to discover the cognition commonality among several people while observing a physical object by analysis of large amount of descriptive about the object. The methodology contains four major tasks: (1) object description corpus collection, (2) linguistic processing of the corpus, (3) analysis of the descriptive sentences, and (4) building the cognition commonality model. The description of an object can be decomposed into several partial structure patterns (PSP) which are the combination of major component keywords plus the corresponding attributes. Perform statistical analysis on the PSP reveal the structural of the cognition commonality about the target object. Taking the wild birds in Taiwan as experiment target, our study had discovered the common structure of how an object is described. The results are useful as the authors are building a bird inquiring system which allows users to query about a specific bird by input the cognized
  • Keywords
    cognition; natural language processing; speech recognition; statistical analysis; cognition commonality analysis; descriptive sentence analysis; linguistic processing; object description corpus collection; partial structure patterns; statistical analysis; Birds; Buildings; Cognition; Cybernetics; Hidden Markov models; Information analysis; Machine learning; Natural languages; Speech recognition; Speech synthesis; Cognition commonality; Major component keyword; Object description; Partial structure pattern;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2008 International Conference on
  • Conference_Location
    Kunming
  • Print_ISBN
    978-1-4244-2095-7
  • Electronic_ISBN
    978-1-4244-2096-4
  • Type

    conf

  • DOI
    10.1109/ICMLC.2008.4620954
  • Filename
    4620954