• DocumentCode
    166608
  • Title

    Mining Hidden Concepts: Using Short Text Clustering and Wikipedia Knowledge

  • Author

    Cheng-Lin Yang ; Benjamasutin, Nuttakorn ; Chen-Burger, Yun-Heh

  • Author_Institution
    Sch. of Inf., Univ. of Edinburgh, Edinburgh, UK
  • fYear
    2014
  • fDate
    13-16 May 2014
  • Firstpage
    675
  • Lastpage
    680
  • Abstract
    In recent years, there has been a rapidly increasing use of social networking platforms in the forms of short-text communication. However, due to the short-length of the texts used, the precise meaning and context of these texts are often ambiguous. To address this problem, we have devised a new community mining approach that is an adaptation and extension of text clustering, using Wikipedia as background knowledge. Based on this method, we are able to achieve a high level of precision in identifying the context of communication. Using the same methods, we are also able to efficiently identify hidden concepts in Twitter texts. Using Wikipedia as background knowledge considerably improved the performance of short text clustering.
  • Keywords
    data mining; pattern clustering; social networking (online); text analysis; Twitter texts; Wikipedia knowledge; background knowledge; communication context; community mining approach; hidden concepts mining; short text clustering; short-text communication; social networking platforms; Clustering algorithms; Communities; Electronic publishing; Encyclopedias; Internet; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advanced Information Networking and Applications Workshops (WAINA), 2014 28th International Conference on
  • Conference_Location
    Victoria, BC
  • Print_ISBN
    978-1-4799-2652-7
  • Type

    conf

  • DOI
    10.1109/WAINA.2014.109
  • Filename
    6844716