Scaling Understanding up to Mental Spaces

Mental Space Theory (Fauconnier, 1985) encompasses a wide variety of complex linguistics phenomena that are largely ignored in today’s natural language processing systems. These phenomena include conditionals (e.g. If sentences), embedded discourse, and other natural language utterances whose interpretation depends on cognitive partitioning of contextual knowledge. A unification-based formalism, Embodied Construction Grammar (ECG) (Chang et al., 2002a) took initial steps to include space as a primitive type, but most of the details are yet to be worked out. The goal of this paper is to present a scalable computational account of mental spaces based on the Neural Theory of Language (NTL) simulation-based understanding framework (Narayanan, 1999; Chang et al., 2002b). We introduce a formalization of mental spaces based on ECG, and describe how this formalization fits into the NTL framework. We will also use English Conditionals as a case study to show how mental spaces can be parameterized from language.