Building a shared world: mapping distributional to model-theoretic semantic spaces
Aurélie Herbelot, Eva Maria Vecchi · 2015
In this paper, we introduce an approach to automatically map a standard distributional semantic space onto a set-theoretic model.We predict that there is a functional relationship between distributional information and vectorial concept representations in which dimensions are predicates and weights are generalised quantifiers.In order to test our prediction, we learn a model of such relationship over a publicly available dataset of feature norms annotated with natural language quantifiers.Our initial experimental results show that, at least for domain-specific data, we can indeed map between formalisms, and generate high-quality vector representations which encapsulate set overlap information.We further investigate the generation of natural language quantifiers from such vectors.