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Selectional preferences (SPs) are widely used in natural language processing as a rich source of semantic information. While SPs have been traditionally induced from textual data, human lexical acquisition is known to rely on both linguistic and perceptual experience. We present the first SP learning method that simultaneously draws knowledge from text, images and videos, using image and video descriptions to obtain visual features. Our results show that it outperforms linguistic and visual models in isolation, as well as the existing SP induction approaches.