When building the training set for such a word predictor w, instead of simply dividing objects into w and ¬w instances, we label each object with a real-valued similarity obtained from cosine similarity between w and v in a distributional vector space, where v is the word that was used to refer to the object.
we task the model with jointly learning similarities and referential appropriateness, by training it with Ridge regression on a continuous output space.