Mapping semantic knowledge for unsupervised text categorisation
Paper
Paper/Presentation Title | Mapping semantic knowledge for unsupervised text categorisation |
---|---|
Presentation Type | Paper |
Authors | Tao, Xiaohui (Author), Li, Yuefeng (Author), Zhang, Ji (Author) and Yong, Jianming (Author) |
Editors | Wang, Hua and Rui, Zhang |
Journal or Proceedings Title | Conferences in Research and Practice in Information Technology (CRPIT) |
ERA Conference ID | 42492 |
Journal Citation | 137, pp. 51-60 |
Number of Pages | 10 |
Year | 2013 |
Place of Publication | Sydney, Australia |
ISBN | 9781921770227 |
Web Address (URL) of Paper | http://crpit.com/confpapers/CRPITV137Tao.pdf |
Conference/Event | 24th Australasian Database Conference (ADC 2013) |
Australasian Database Conference | |
Event Details | Australasian Database Conference ADC Rank B B B B B B B B B B B B B B B B B B B B B B B B B |
Event Details | 24th Australasian Database Conference (ADC 2013) Event Date 29 Jan 2013 to end of 01 Feb 2013 Event Location Adelaide, Australia |
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. |
Keywords | text categorisation; knowledge mapping; ontology |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
460208. Natural language processing | |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | Centre for Systems Biology |
Queensland University of Technology | |
School of Information Systems | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q2505/mapping-semantic-knowledge-for-unsupervised-text-categorisation
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