Word Sense Disambiguation (WSD) is an important problem in Natural Language Processing. Supervised WSD involves assigning a sense from some sense inventory to each occurrence of an ambiguous word. Verb sense distinctions often depend on the distinctions in the semantics of the target verb's arguments. Therefore, some method of capturing their semantics is crucial to the success of a VSD system. In this paper we propose a novel approach to encoding the semantics of the noun arguments of a verb. This approach involves extracting various semantic properties of that verb from a large text corpus. We contrast our approach with the traditional methods and show that it performs better while the only resources it requires are a large corpus and a dependency parser.