Grounding Distributional Semantics in the Visual World

This is the authors' abstract. We don't add key points for this paper.

Distributional semantic models build vector‐based word meaning representations on top of contextual information extracted from large collections of text. Object recognition methods from computer vision derive vector‐based representations of visual content from natural images. This article reviews how methods from computer vision are exploited to tackle the fundamental problem of grounding distributional semantic models, bringing them closer to providing a full‐fledged computational account of meaning.