Unsupervised multi-label text classification using a world knowledge ontology

Tao, Xiaohui ORCID: https://orcid.org/0000-0002-0020-077X and Li, Yuefeng and Lau, Raymond Y. K. and Wang, Hua (2012) Unsupervised multi-label text classification using a world knowledge ontology. In: 16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD 2012), 29 May - 1 Jun 2012, Kuala Lumpur, Malaysia.

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Abstract

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 classication methods, using a real-world document collection and based on the ground truth encoded by human experts.

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Item Type:	Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed:	Yes
Item Status:	Live Archive
Additional Information:	Accepted version deposited in accordance with the copyroight policy of the publisher. Series title: Lecture Notes in Computer Science Vol 7301, Pt 1.
Faculty/School / Institute/Centre:	Historic - Faculty of Sciences - Department of Maths and Computing (Up to 30 Jun 2013)
Faculty/School / Institute/Centre:	Historic - Faculty of Sciences - Department of Maths and Computing (Up to 30 Jun 2013)
Date Deposited:	29 Apr 2013 04:57
Last Modified:	12 Sep 2014 05:51
Uncontrolled Keywords:	unsupervised classification; text classification; ontology
Fields of Research (2008):	08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080107 Natural Language Processing 08 Information and Computing Sciences > 0806 Information Systems > 080606 Global Information Systems
Socio-Economic Objectives (2008):	B Economic Development > 89 Information and Communication Services > 8903 Information Services > 890301 Electronic Information Storage and Retrieval Services
Identification Number or DOI:	https://doi.org/10.1007/978-3-642-30217-6_40
URI:	http://eprints.usq.edu.au/id/eprint/20910

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