Inference with Distributional Semantic Models

Distributional Semantic Models have emerged as a strong theoretical and practical approach to model the meaning of words. Indeed, an increasing body of work has proved their value in accounting for a wide range of semantic phenomena. Yet, it is still unclear how we can use the semantic information contained in these representations to support the natural inferences that we produce in our every day usage of natural language. In this thesis, I explore a selection of challenging relations that exemplify these inferential processes. To this end, on one hand, I present new publicly available datasets to allow for their empirical treatment. On the other, I introduce computational models that can account for these relations using distributional representations as their conceptual knowledge repository. The performance of these models demonstrate the feasibility of this approach while leaving room for improvement in future work.