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
Link To Document