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Compositional distributional semantics is a subfield of Computational Linguistics which investigates methods for representing the meanings of phrases and sentences. In this paper, we explore implementations of a framework based on Combinatory Categorial Grammar (CCG), in which words with certain grammatical types have meanings represented by multilinear maps (i.e. multi-dimensional arrays, or tensors). An obstacle to full implementation of the framework is the size of these tensors. We examine the performance of lower dimensional approximations of transitive verb tensors on a sentence plausibility/selectional preference task. We find that the matrices perform as well as, and sometimes even better than, full tensors, allowing a reduction in the number of parameters needed to model the framework.

Reducing Dimensions of Tensors in Type-Driven Distributional Semantics