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Automatic identification of cross-document structural relationships

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. C...

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Main Authors: Kumar, Yogan Jaya, Salim, Naomie, Hamza, Ahmed, Abuobieda, Albarraa
Format: Conference or Workshop Item
Published: 2012
Online Access:http://eprints.utm.my/id/eprint/34010/
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spelling my.utm.340102017-09-07T04:14:13Z http://eprints.utm.my/id/eprint/34010/ Automatic identification of cross-document structural relationships Kumar, Yogan Jaya Salim, Naomie Hamza, Ahmed Abuobieda, Albarraa 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. 2012 Conference or Workshop Item PeerReviewed 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).
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
description 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.
format Conference or Workshop Item
author Kumar, Yogan Jaya
Salim, Naomie
Hamza, Ahmed
Abuobieda, Albarraa
spellingShingle Kumar, Yogan Jaya
Salim, Naomie
Hamza, Ahmed
Abuobieda, Albarraa
Automatic identification of cross-document structural relationships
author_facet Kumar, Yogan Jaya
Salim, Naomie
Hamza, Ahmed
Abuobieda, Albarraa
author_sort Kumar, Yogan Jaya
title Automatic identification of cross-document structural relationships
title_short Automatic identification of cross-document structural relationships
title_full Automatic identification of cross-document structural relationships
title_fullStr Automatic identification of cross-document structural relationships
title_full_unstemmed Automatic identification of cross-document structural relationships
title_sort automatic identification of cross-document structural relationships
publishDate 2012
url http://eprints.utm.my/id/eprint/34010/
_version_ 1643649492734443520
score 13.209306

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