A hierarchical test categorization approach and its application to FRT expansion
Domonkos Tikk, György Biró · International Conference on Intelligent Information Processing · 2004
Text categorization is the classiflcation to assign a text document to an appropriate category in a predeflned set of categories. This paper focuses on the special case when categories are organized in hierarchy. We presents a new approach on this recently emerged subfleld of text categorization. The algorithm applies an iterative learning module that allow of gradually creating a classifler by trial-and-error-like method. Experimental results performed on three document corpora (including the wellknown Reuters-21578, and 20 newsgroups data sets) with several topic hierarchies show that our approach outperforms existing ones by up to 10%. We also indicate another application of the method on the fleld of fuzzy relational thesauri (FRT): the expansion of knowledge base can be supported in a cost-efiective way.