Word sense disambiguation is typically phrased as the task of labeling a word in context with the best-fitting sense from a sense inventory such as WordNet. While questions have often been raised over the choice of sense inventory, computational linguists have readily accepted the bestfitting sense methodology despite the fact that the case for discrete sense boundaries is widely disputed by lexical semantics researchers. This paper studies graded word sense assignment, based on a recent dataset of graded word sense annotation.