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.
![]() |
PDF (Accepted Version)
UnsupervisedClassification_#95_TAO.pdf Download (1MB) |
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.
![]() |
Statistics for this ePrint Item |
Actions (login required)
![]() |
Archive Repository Staff Only |