Abstract
The paper concerns one of relevant issues related to the handling of textual information, that is the dominant form of information in many real world problems, for providing support for decision making. We discuss the issue of text document categorization that is a prerequisite for further analyses. We indicate how the use of fuzzy linguistic summaries for text categorization may help the decision maker to have documents classified in a human consistent way into categories, which in turn should greatly help him or her extract relevant information and knowledge from textual documents available, and then use them to arrive at a better decision in a more effective and efficient way. We indicate that the solutions proposed can be of use for enhancing the power of so-called document driven decision support systems.
Research supported by KBN Grant 4 T11F 012 25.
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Kacprzyk, J., Zadrożny, S. (2005). Fuzzy Linguistic Summaries in Text Categorization for Human-Consistent Document-Driven Decision Support Systems. In: Reusch, B. (eds) Computational Intelligence, Theory and Applications. Advances in Soft Computing, vol 33. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31182-3_24
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DOI: https://doi.org/10.1007/3-540-31182-3_24
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