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Distributional models are derived from co-occurrences in a corpus, where onlya small proportion of all possible plausible co-occurrences will be observed.This results in a very sparse vector space, requiring a mechanism for inferringmissing knowledge. Most methods face this challenge in ways that render theresulting word representations uninterpretable, with the consequence thatsemantic composition becomes hard to model. In this paper we explore analternative which involves explicitly inferring unobserved co-occurrences usingthe distributional neighbourhood. We show that distributional inferenceimproves sparse word representations on several word similarity benchmarks anddemonstrate that our model is competitive with the state-of-the-art foradjective-noun, noun-noun and verb-object compositions while being fullyinterpretable.

Improving Sparse Word Representations with Distributional Inference for Semantic Composition