Automated feature choice is essential for text categorization to range back the feature size and to hurry up the educational method of classifiers. Allocated engineering tasks are often conducted using process models. In this circumstance, it is necessary that these models do not contain structural or terminological inconsistencies. To this end, many automatic analysis techniques have been proposed to provide quality assurance. While appropriate properties of control flow can be checked in an automated fashion, there is a lack of techniques addressing textual quality. More particularly, there is currently no technique available for handling the issue of lexical ambiguity caused by homonyms and synonyms. In this paper, we tackle this research gap and intend a modus operandi that detect and resolve lexical ambiguities in practice models.

Text categorization, X-Drop Algorithm

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