The vast majority of work on word senses has relied on predefined sense inventories and an annotation schema where each word instance is tagged with the best fitting sense. This paper examines the case for a graded notion of word meaning in two experiments, one which uses WordNet senses in a graded fashion, contrasted with the “winner takes all” annotation, and one which asks annotators to judge the similarity of two usages. We find that the graded responses correlate with annotations from previous datasets, but sense assignments are used in a way that weakens the case for clear cut.
