An inference-based model of word meaning in context as a paraphrase distribution

Graded models of word meaning in context characterize the meaning of individual usages (occurrences) without reference to dictionary senses. We introduce a novel approach that frames the task of computing word meaning in context as a probabilistic inference problem. The model represents the meaning of a word as a probability distribution over potential paraphrases, inferred using an undirected graphical model. Evaluated on paraphrasing tasks, the model achieves state-of-the-art performance.