Distributional semantic models provide vector representations for words by gathering co-occurrence frequencies from corpora of text. Compositional distributional models extend these representations from words to phrases and sentences. In categorical compositional distributional semantics these representations are built in such a manner that meanings of phrases and sentences are functions of their grammatical structure and the meanings of the words therein. These models have been applied to reasoning about phrase and sentence level similarity. In this paper, we argue for and prove that these models can also be used to reason about phrase and sentence level entailment. We provide preliminary experimental results on a toy entailment dataset.
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