Distributional semantics from text and images

We present a distributional semantic model combining textand image-based features. We evaluate this multimodal semantic model on simulating similarity judgments, concept clustering and the BLESS benchmark. When integrated with the same core text-based model, image-based features are at least as good as further text-based features, and they capture different qualitative aspects of the tasks, suggesting that the two sources of information are complementary.