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...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
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2012
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| _version_ | 1796857301237235712 |
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| author | Kumar, Yogan Jaya Salim, Naomie Hamza, Ahmed Abuobieda, Albarraa |
| author_facet | Kumar, Yogan Jaya Salim, Naomie Hamza, Ahmed Abuobieda, Albarraa |
| author_sort | Kumar, Yogan Jaya |
| collection | ePrints |
| 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. |
| first_indexed | 2024-03-05T18:55:37Z |
| format | Conference or Workshop Item |
| id | utm.eprints-34010 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T18:55:37Z |
| publishDate | 2012 |
| record_format | dspace |
| spelling | utm.eprints-340102017-09-07T04:14:13Z http://eprints.utm.my/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). |
| spellingShingle | Kumar, Yogan Jaya Salim, Naomie Hamza, Ahmed Abuobieda, Albarraa Automatic identification of cross-document structural relationships |
| title | 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_short | Automatic identification of cross-document structural relationships |
| title_sort | automatic identification of cross document structural relationships |
| work_keys_str_mv | AT kumaryoganjaya automaticidentificationofcrossdocumentstructuralrelationships AT salimnaomie automaticidentificationofcrossdocumentstructuralrelationships AT hamzaahmed automaticidentificationofcrossdocumentstructuralrelationships AT abuobiedaalbarraa automaticidentificationofcrossdocumentstructuralrelationships |