e-ISSN 2231-8542
ISSN 1511-3701

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Using SVMs for Classification of Cross-Document Relationships

Yogan Jaya Kumar, Naomie Salim, Ahmed Hamza Osman and Albaraa Abuobieda

Pertanika Journal of Tropical Agricultural Science, Volume 21, Issue 1, January 2013

Keywords: CST relation, multi-document, rhetorical relation, SVMs

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Cross-document Structure Theory (CST) has recently been proposed to facilitate tasks related to multi-document analysis. Classifying and identifying the CST relationships between sentences across topically related documents have since been proven as necessary. However, there have not been sufficient studies presented in literature to automatically identify these CST relationships. In this study, a supervised machine learning technique, i.e. Support Vector Machines (SVMs), was applied to identify four types of CST relationships, namely “Identity”, “Overlap”, “Subsumption”, and “Description” on the datasets obtained from CSTBank corpus. The performance of the SVMs classification was measured using Precision, Recall and F-measure. In addition, the results obtained using SVMs were also compared with those from the previous literature using boosting classification algorithm. It was found that SVMs yielded better results in classifying the four CST relationships.

ISSN 1511-3701

e-ISSN 2231-8542

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