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Cross-document structural relationship identification using supervised machine learning

Multi document analysis has been a field of interest for decades and is still being actively researched until today. One example of such analysis could be for the task of multi document summarization which is meant to represent the concise description of the original documents. In this paper, we wil...

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Main Authors: Kumar, Yogan Jaya, Salim, Naomie, Raza, Basit
Format: Article
Published: 2012
Subjects:
QA76 Computer software
Online Access:http://eprints.utm.my/id/eprint/46756/
https://dx.doi.org/10.1016/j.asoc.2012.06.017
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spelling my.utm.467562017-09-19T03:42:14Z http://eprints.utm.my/id/eprint/46756/ Cross-document structural relationship identification using supervised machine learning Kumar, Yogan Jaya Salim, Naomie Raza, Basit QA76 Computer software Multi document analysis has been a field of interest for decades and is still being actively researched until today. One example of such analysis could be for the task of multi document summarization which is meant to represent the concise description of the original documents. 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 several relationships between pairs of sentences from different documents. Among them, we focus on four relations namely “Identity”, “Overlap”, “Subsumption”, and “Description”. Our aim is to automatically identify these CST relationships. We applied three machine learning techniques, i.e. SVM, neural network and our proposed case-based reasoning (CBR) model. Comparison between these techniques shows that the proposed CBR model yields better results. 2012 Article PeerReviewed Kumar, Yogan Jaya and Salim, Naomie and Raza, Basit (2012) Cross-document structural relationship identification using supervised machine learning. Applied Soft Computing, 12 . pp. 3124-3131. ISSN 1568-4946 https://dx.doi.org/10.1016/j.asoc.2012.06.017
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/
topic QA76 Computer software
spellingShingle QA76 Computer software
Kumar, Yogan Jaya
Salim, Naomie
Raza, Basit
Cross-document structural relationship identification using supervised machine learning
description Multi document analysis has been a field of interest for decades and is still being actively researched until today. One example of such analysis could be for the task of multi document summarization which is meant to represent the concise description of the original documents. 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 several relationships between pairs of sentences from different documents. Among them, we focus on four relations namely “Identity”, “Overlap”, “Subsumption”, and “Description”. Our aim is to automatically identify these CST relationships. We applied three machine learning techniques, i.e. SVM, neural network and our proposed case-based reasoning (CBR) model. Comparison between these techniques shows that the proposed CBR model yields better results.
format Article
author Kumar, Yogan Jaya
Salim, Naomie
Raza, Basit
author_facet Kumar, Yogan Jaya
Salim, Naomie
Raza, Basit
author_sort Kumar, Yogan Jaya
title Cross-document structural relationship identification using supervised machine learning
title_short Cross-document structural relationship identification using supervised machine learning
title_full Cross-document structural relationship identification using supervised machine learning
title_fullStr Cross-document structural relationship identification using supervised machine learning
title_full_unstemmed Cross-document structural relationship identification using supervised machine learning
title_sort cross-document structural relationship identification using supervised machine learning
publishDate 2012
url http://eprints.utm.my/id/eprint/46756/
https://dx.doi.org/10.1016/j.asoc.2012.06.017
_version_ 1643652130914959360
score 13.211869

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