A Compositional Distributional Inclusion Hypothesis

Abstract

AbstractThe distributional inclusion hypothesis provides a pragmatic way of evaluating entailment between word vectors as represented in a distributional model of meaning. In this paper, we extend this hypothesis to the realm of compositional distributional semantics, where meanings of phrases and sentences are computed by composing their word vectors. We present a theoretical analysis for how feature inclusion is interpreted under each composition operator, and propose a measure for evaluating entailment at the phrase/sentence level. We perform experiments on four entailment datasets, showing that intersective composition in conjunction with our proposed measure achieves the highest performance.