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Automatic identification of cross-document structural relationships

Kumar, Yogan Jaya and Salim, Naomie and Hamza, Ahmed and Abuobieda, Albarraa (2012) Automatic identification of cross-document structural relationships. In: The International Conference on Information Retrieval and Knowledge Management (CAMP'12).

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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.

Item Type:Conference or Workshop Item (Paper)
Divisions:Computer Science and Information System
ID Code:34010
Deposited By: Liza Porijo
Deposited On:21 Aug 2017 06:47
Last Modified:07 Sep 2017 04:14

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