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
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);
Conference_Titel :
Information Retrieval & Knowledge Management (CAMP), 2012 International Conference on
Conference_Location :
Kuala Lumpur
Print_ISBN :
978-1-4673-1091-8
DOI :
10.1109/InfRKM.2012.6204977