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According to the distributional inclusion hypothesis, entailment betweenwords can be measured via the feature inclusions of their distributionalvectors. In recent work, we showed how this hypothesis can be extended fromwords to phrases and sentences in the setting of compositional distributionalsemantics. This paper focuses on inclusion properties of tensors; its maincontribution is a theoretical and experimental analysis of how featureinclusion works in different concrete models of verb tensors. We presentresults for relational, Frobenius, projective, and holistic methods and comparethem to the simple vector addition, multiplication, min, and max models. Thedegrees of entailment thus obtained are evaluated via a variety of existingword-based measures, such as Weed's and Clarke's, KL-divergence, APinc,balAPinc, and two of our previously proposed metrics at the phrase/sentencelevel. We perform experiments on three entailment datasets, investigating whichversion of tensor-based composition achieves the highest performance whencombined with the sentence-level measures.

Distributional Inclusion Hypothesis for Tensor-based Composition