Mapping semantic knowledge for unsupervised text categorisation

Tao, Xiaohui and Li, Yuefeng and Zhang, Ji and Yong, Jianming (2013) Mapping semantic knowledge for unsupervised text categorisation. In: 24th Australasian Database Conference (ADC 2013), 29 Jan-1 Feb 2013, Adelaide, Australia.

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Abstract

Text categorisation is challenging, due to the complex structure with heterogeneous, changing topics in documents. The performance of text categorisation relies on the quality of samples, effectiveness of document features, and the topic coverage of categories, depending on the employing strategies; supervised or unsupervised; single labelled or multi-labelled. Attempting to deal with these reliability issues in text categorisation, we propose an unsupervised multi-labelled text categorisation approach that maps the local knowledge in documents to global knowledge in a world ontology to optimise categorisation result. The conceptual framework of the approach consists of three modules; pattern mining for feature extraction; feature-subject mapping for categorisation; concept generalisation for optimised categorisation. The approach has been promisingly evaluated by compared with typical text categorisation methods, 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: c 2013, Australian Computer Society, Inc. (ADC2013), Series title: Conferences in Research and Practice in Information Technology (CRPIT), Vol. 137, Hua Wang and Rui Zhangs, Ed. Reproduction for academic, not-for-profit purposes permitted provided this text is included.
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 02 Mar 2014 04:54
Last Modified: 17 Mar 2015 05:39
Uncontrolled Keywords: text categorisation; knowledge mapping; ontology
Fields of Research : 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
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
URI: http://eprints.usq.edu.au/id/eprint/24897

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