Enhance web pages genre identification using neighboring pages

Zhu, Jia, Zhou, Xiaofang and Fung, Gabriel (2011). Enhance web pages genre identification using neighboring pages. In: Web Information System Engineering, WISE 2011. 12th International Conference on Web Information System Engineering, WISE 2011, Sydney, NSW, (282-289). 13-14 October 2011. doi:10.1007/978-3-642-24434-6_23


Author Zhu, Jia
Zhou, Xiaofang
Fung, Gabriel
Title of paper Enhance web pages genre identification using neighboring pages
Conference name 12th International Conference on Web Information System Engineering, WISE 2011
Conference location Sydney, NSW
Conference dates 13-14 October 2011
Proceedings title Web Information System Engineering, WISE 2011   Check publisher's open access policy
Journal name Lecture Notes in Computer Science   Check publisher's open access policy
Series Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Place of Publication Heidelberg, Germany
Publisher Springer
Publication Year 2011
Year available 2011
Sub-type Fully published paper
DOI 10.1007/978-3-642-24434-6_23
ISBN 9783642244339
ISSN 0302-9743
1611-3349
Volume 6997
Start page 282
End page 289
Total pages 8
Language eng
Abstract/Summary Recently web pages genre identification attracts more attentions because of its importance in web searching. Most of existing works used the features extracted from web pages and applied machine learning approaches like SVM as classifier to identify the genre of web pages. However, in the case where web pages do not contain enough information, such an approach may not work well. In this paper, we consider to tackle genre identification in such situations. We propose a link-based graph model that taking into account neighboring pages but greatly reducing the noisy information by selecting an appropriate subset of neighboring pages. We evaluated this neighboring pages based classifier with other classifiers. The experiments conducted on two known corpora, and the favorable results indicated that our proposed approach is feasible.
Subjects 1700 Computer Science
2614 Theoretical Computer Science
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Conference Paper
Sub-type: Fully published paper
Collection: School of Information Technology and Electrical Engineering Publications
 
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Created: Thu, 28 Nov 2013, 09:34:11 EST by System User on behalf of School of Information Technol and Elec Engineering