Paper: A Generative Model of Vector Space Semantics

ACL ID W13-3211
Title A Generative Model of Vector Space Semantics
Venue Continuous Vector Space Models and their Compositionality
Year 2013

We present a novel compositional, gener- ative model for vector space representa- tions of meaning. This model reformulates earlier tensor-based approaches to vector space semantics as a top-down process, and provides efficient algorithms for trans- formation from natural language to vectors and from vectors to natural language. We describe procedures for estimating the pa- rameters of the model from positive exam- ples of similar phrases, and from distribu- tional representations, then use these pro- cedures 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 ap- proaches in both settings.