A Genetic-CBR Approach for Cross-Document Relationship Identification

Jaya Kumar, Yogan (2012) A Genetic-CBR Approach for Cross-Document Relationship Identification. In: Advanced Machine Learning Technologies and Applications. Communications in Computer and Information Science, 322 (322). SPRINGER VERLAG, BERLIN HEIDELBERG, pp. 182-192. ISBN 978-3-642-35325-3

PDF AMLTA_2012_revised.pdf Restricted to Repository staff only Download (505kB) | Request a copy

Abstract

Various applications concerning multi document has emerged recently. Information across topically related documents can often be linked. Cross-document Structure Theory (CST) analyzes the relationships that exist between sentences across related documents. However, most of the existing works rely on human experts to identify the CST relationships. In this work, we aim to automatically identify some of the CST relations using supervised learning method. We propose Genetic-CBR approach which incorporates genetic algorithm (GA) to improve the case base reasoning (CBR) classification. GA is used to scale the weights of the data features used by the CBR classifier. We perform the experiments using the datasets obtained from CSTBank corpus. Comparison with other learning methods shows that the proposed method yields better results.

Item Type:	Book Chapter
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Information and Communication Technology > Department of Industrial Computing
Depositing User:	YOGAN JAYA KUMAR
Date Deposited:	20 Apr 2013 15:02
Last Modified:	28 May 2015 03:44
URI:	http://eprints.utem.edu.my/id/eprint/6671
Statistic Details:	View Download Statistic

Actions (login required)

View Item