The web has caused an explosion of documents, requiring the need for an automated text categorization system. This paper explores the notion of semantic feature selection by employing WordNet [Introduction to WordNet: An On-line Lexical Database], a lexical database. The proposed semantic approach employs noun synonyms and word senses for feature selection to select terms that are semantically representative of a category of documents. The categorical sense disambiguation extends the use of WordNet, which has been typically used for text retrieval and word sense disambiguation [A WordNet-based Algorithm for Word Sense Disambiguation]. Our experiments on the Reuters-21578 dataset have shown that automated semantic feature selection is able to perform better than well known statistical feature selection methods, Information Gain and Chi-Square as a feature selection method.