We propose a method that uses information from WordNet glosses to assign semantic tags to individual word meanings, rather than to entire words. The produced lists of annotated words will be used in sentiment annotation of texts and phrases and in other NLP tasks. The method was implemented in the Semantic Tag Extraction Program (STEP) and evaluated on the category of sentiment (positive, negative or neutral) using two human-annotated lists. The lists were first compared to each other and then used to assess the accuracy of the proposed system. We argue that significant disagreement on sentiment tags between the two human-annotated lists reflects a naturally occurring ambiguity of words located on the periphery of the category of sentiment. The category of sentiment, thus, is believed to be structured as a fuzzy set. Finally, we evaluate the generalizability of STEP to other semantic categories on the example of the category of words denoting increase/decrease in magnitude, intensity or quality of some state or process. The implications of this study for both semantic tagging system development and for performance evaluation practices are discussed.