• DocumentCode
    2033267
  • Title

    Classification of brand names based on n-grams

  • Author

    Warintarawej, P. ; Laurent, A. ; Pompidor, P. ; Laurent, B.

  • Author_Institution
    LIRMM, Univ. Montpellier 2, Montpellier, France
  • fYear
    2010
  • fDate
    7-10 Dec. 2010
  • Firstpage
    12
  • Lastpage
    17
  • Abstract
    Supervised classification has been extensively addressed in the literature as it has many applications, especially for text categorization or web content mining where data are organized through a hierarchy. On the other hand, the automatic analysis of brand names can be viewed as a special case of text management, although such names are very different from classical data. They are indeed often neologisms, and cannot be easily managed by existing NLP tools. In our framework, we aim at automatically analyzing such names and at determining to which extent they are related to some concepts that are hierarchically organized. The system is based on the use of character n-grams. The targeted system is meant to help, for instance, to automatically determine whether a name sounds like being related to ecology.
  • Keywords
    data mining; learning (artificial intelligence); marketing; pattern classification; text analysis; Web content mining; brand name classification; n-grams; supervised classification; text categorization; text management; Accuracy; Buildings; Companies; Pattern recognition; Thesauri; Training; Training data; Brand Names; Hierarchies; Textual Classification; n-grams;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Soft Computing and Pattern Recognition (SoCPaR), 2010 International Conference of
  • Conference_Location
    Paris
  • Print_ISBN
    978-1-4244-7897-2
  • Type

    conf

  • DOI
    10.1109/SOCPAR.2010.5685842
  • Filename
    5685842