A Generative Model of Vector Space Semantics

Jacob Andreas, Zoubin Ghahramani

Abstract: We present a novel compositional, generative model for vector space representations of meaning. This model reformulates earlier tensor-based approaches to vector space semantics as a top-down process, and provides efficient algorithms for transformation from natural language to vectors and from vectors to natural language. We describe procedures for estimating the parameters of the model from positive examples of similar phrases, and from distributional representations, then use these procedures to obtain similarity judgments for a set of adjective-noun pairs. The model’s estimation of the similarity of these pairs correlates well with human annotations, demonstrating a substantial improvement over several existing compositional approaches in both settings.