Abstract
Any conceptual computer or computing with words (CW) system is expected to represent its results with a reasonable output, such as a sentence in natural language. A CW system is required to translate the fuzzy values provided as its result into words. This paper explores different similarity measures as well as linguistic approximation methods for generating natural language sentences for CW systems. Methods in both approaches are evaluated through various measures such as fuzziness, specificity, validity, and sigma-count. Evaluation results suggest certain linguistic methods may result in complex and incomprehensible phrases in natural language. They might even include an invalid linguistic term in their linguistic approximation. On the other hand, methods based on similarity measures may result in simpler and more comprehensible linguistic terms but might not be able to select a perfect match.
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