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Classifying XML documents by using genre features.

Clark, Malcolm; Watt, Stuart


Malcolm Clark

Stuart Watt


A.M. Tjoa

R.R. Wagner


The categorization of documents is traditionally topic-based. This paper presents a complementary analysis of research and experiments on genre to show that encouraging results can be obtained by using genre structure (form) features. We conducted an experiment to assess the effectiveness of using extensible mark-up language (XML) tag information, and part-of-speech (P-O-S) features, for the classification of genres, testing the hypothesis that if a focus on genre can lead to high precision on normal textual documents, then good results can be achieved using XML tag information in addition to P-O-S information. An experiment was carried out on a subsection of the initiative for the evaluation of XML (INEX) 1.4 collection. The features were extracted and documents were classified using machine learning algorithms, which yielded encouraging results for logistic regression and neural networks. We propose that utilizing these features and training a classifier may benefit retrieval for most world wide web (WWW) technologies such as XML and extensible hypertext markup language) XHTML.


CLARK, M. and WATT, S. 2007. Classifying XML documents by using genre features. In Tjoa, A.M. and Wagner, R.R. (eds.) Proceedings of the 18th International workshop on database and expert systems applications (DEXA 2007), 3-7 September 2007, Regensburg, Germany. Los Alamitos: IEEE Computer Society [online], article number 4312894, pages 242-248. Available from:

Conference Name 18th International workshop on database and expert systems applications (DEXA 2007)
Conference Location Regensburg, Germany
Start Date Sep 3, 2007
End Date Sep 7, 2007
Acceptance Date Sep 30, 2007
Online Publication Date Sep 30, 2007
Publication Date Sep 30, 2007
Deposit Date Mar 11, 2015
Publicly Available Date Mar 11, 2015
Print ISSN 1529-4188
Electronic ISSN 2378-3915
Publisher IEEE Computer Society
Article Number 4312894
Pages 242-248
Series Title Proceedings of the international workshop on database and expert systems applications
Series ISSN 2378-3915
ISBN 9780769529325
Keywords XML; Testing; Feature extraction; Data mining; Machine learning algorithms; Logistics; Neural networks; Web sites; World Wide Web; Markup languages
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