Unsupervised multi-label text classification using a world knowledge ontology

Tao, Xiaohui, Li, Yuefeng, Lau, Raymond Y.K., & Wang, Hua (2012) Unsupervised multi-label text classification using a world knowledge ontology. Lecture Notes in Computer Science, 7301 LNAI(Part 1), pp. 480-492.

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The development of text classification techniques has been largely promoted in the past decade due to the increasing availability and widespread use of digital documents. Usually, the performance of text classification relies on the quality of categories and the accuracy of classifiers learned from samples. When training samples are unavailable or categories are unqualified, text classification performance would be degraded. In this paper, we propose an unsupervised multi-label text classification method to classify documents using a large set of categories stored in a world ontology. The approach has been promisingly evaluated by compared with typical text classification methods, using a real-world document collection and based on the ground truth encoded by human experts.

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2 citations in Scopus
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ID Code: 51027
Item Type: Journal Article
Refereed: Yes
Keywords: Text classification techniques, Performance
DOI: 10.1007/978-3-642-30217-6_40
ISSN: 0302-9743
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2012 Springer-Verlag Berlin Heidelberg
Deposited On: 24 Jun 2012 23:22
Last Modified: 18 Jan 2015 23:44

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