Learning adjective meanings with a tensor-based skip-gram model

Abstract

We present a compositional distributional semantic model which is an implementation of the tensor-based framework of Coecke et al. (2011). It is an extended skip-gram model (Mikolov et al., 2013) which we apply to adjective-noun combinations, learning nouns as vectors and adjectives as matrices. We also propose a novel measure of adjective similarity, and show that adjective matrix representations lead to improved performance in adjective and adjective-noun similarity tasks, as well as in the detection of semantically anomalous adjective-noun pairs.