• DocumentCode
    2167140
  • Title

    Automatic identification of cross-document structural relationships

  • Author

    Kumar, Yogan Jaya ; Salim, Naomie ; Hamza, Ahmed ; Abuobieda, Albarraa

  • Author_Institution
    Fac. of Inf. & Commun. Technol., Univ. Teknikal Malaysia Melaka, Durian Tunggal, Malaysia
  • fYear
    2012
  • fDate
    13-15 March 2012
  • Firstpage
    26
  • Lastpage
    29
  • Abstract
    Analysis on inter-document relationship is one of the important studies in multi document analysis. In this paper, we will focus on some special properties that multi document articles hold, specifically news articles. Information across news articles reporting on the same story are often related. Cross-document Structure Theory (CST) gives the relationship between pairs of sentences from different documents. For example, two sentences might have relationships such as identical, overlapping or contradicting. Our aim here is to automatically identify some of these CST relationships. We applied the well known machine learning technique, SVMs for this purpose and obtained some comparable results.
  • Keywords
    document handling; information resources; learning (artificial intelligence); support vector machines; CST relationships; SVM; cross-document structural relationship automatic identification; cross-document structure theory; interdocument relationship; machine learning technique; multidocument analysis; news articles; Boosting; Classification algorithms; Computational modeling; Educational institutions; Support vector machines; Training; Cross-document structure theory (CST); Machine learning; Multi document summarization; Rhetorical relation; Support vector machine (SVM);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Retrieval & Knowledge Management (CAMP), 2012 International Conference on
  • Conference_Location
    Kuala Lumpur
  • Print_ISBN
    978-1-4673-1091-8
  • Type

    conf

  • DOI
    10.1109/InfRKM.2012.6204977
  • Filename
    6204977