Jaya Kumar, Yogan and Salim, Naomie and Hamza, Ahmed and Abuobieda, Albarraa (2012) Automatic identification of cross-document structural relationships. In: Proceedings - 2012 International Conference on Information Retrieval and Knowledge Management, CAMP'12. IEEE, New York, USA, pp. 26-29. ISBN 978-146731090-1
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Official URL: http://dx.doi.org/10.1109/InfRKM.2012.6204977
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: | Book Section |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | cross-document structure theory (CST), machine learning, multi document summarization, rhetorical relation, support vector machine (SVM) |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Computer Science and Information System |
| ID Code: | 34547 |
| Deposited By: | INVALID USER |
| Deposited On: | 07 Oct 2013 08:07 |
| Last Modified: | 06 Aug 2017 00:54 |
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