Paper: Models of Semantic Representation with Visual Attributes

ACL ID P13-1056
Title Models of Semantic Representation with Visual Attributes
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

We consider the problem of grounding the meaning of words in the physical world and focus on the visual modality which we represent by visual attributes. We create a new large-scale taxonomy of visual at- tributes covering more than 500 concepts and their corresponding 688K images. We use this dataset to train attribute classi- fiers and integrate their predictions with text-based distributional models of word meaning. We show that these bimodal models give a better fit to human word as- sociation data compared to amodal models and word representations based on hand- crafted norming data.