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 classification methods, using a real-world document collection and based on the ground truth encoded by human experts.
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Tao, X., Li, Y., Lau, R.Y.K., Wang, H. (2012). Unsupervised Multi-label Text Classification Using a World Knowledge Ontology. In: Tan, PN., Chawla, S., Ho, C.K., Bailey, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2012. Lecture Notes in Computer Science(), vol 7301. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30217-6_40
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DOI: https://doi.org/10.1007/978-3-642-30217-6_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30216-9
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