Research Overviews ¬ Rough Set Theory based Automatic Text Categorization...l

Social Science Information Centre, Bonn, Germany,
Nanjing University of Science and Technology, P.R. China and
Nanjing Normal University, P.R. China

Bonn: Social Science Information Center 2006
(Research reports; Volume 8), 151 pp., pbk.
ISSN 1431-5114
ISBN 3-8206-0149-X
Language: English
15,-- EUR

With the growing amount of information available in digital form and from a large number of sources, supporting technologies for automatically organizing this information are in high demand. Automatic text categorization, the automatic assignment of predefined categories to text documents with unknown content, therefore is currently the most popular text processing technique. Machine learning approaches for automatic text categorization, of which rough set theory (RST) is one which gains more and more attention, provide an alternative to traditional statistical approaches and those from knowledge engineering.
This report presents latest results in research on automatic text categorization focussing on language-independent text representation, the application of a RST model combining information retrieval theories and basic rough set theory for the treatment of semantic heterogeneity in information systems, and enhancing automatic text classification by rough set theory based text categorization, dynamic category extension and synonym detection.
The research report was accomplished within the framework of the Sandwich-Programme of the DAAD (German Academic Exchange Service) and out of cooperation between the Social Science Information Centre in Bonn and the Nanjing University of Sciences and Technology/ Nanjing Normal University, P.R. China. It is the English version of the Chinese thesis.

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Research Overviews ¬ Rough Set Theory based Automatic Text Categorization...