Unsupervised multi-label text classification using a world knowledge ontology

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

3 Scopus Citations
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Detail(s)

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining
Subtitle of host publication16th Pacific-Asia Conference, PAKDD 2012, Proceedings
PublisherSpringer Verlag
Pages480-492
Volume7301 LNAI
EditionPART 1
ISBN (Print)9783642302169
Publication statusPublished - 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7301 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Title16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2012
PlaceMalaysia
CityKuala Lumpur
Period29 May - 1 June 2012

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. © 2012 Springer-Verlag.

Citation Format(s)

Unsupervised multi-label text classification using a world knowledge ontology. / Tao, Xiaohui; Li, Yuefeng; Lau, Raymond Y. K. et al.
Advances in Knowledge Discovery and Data Mining: 16th Pacific-Asia Conference, PAKDD 2012, Proceedings. Vol. 7301 LNAI PART 1. ed. Springer Verlag, 2012. p. 480-492 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7301 LNAI).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review