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Distributional models provide a convenient way to model semantics using denseembedding spaces derived from unsupervised learning algorithms. However, thedimensions of dense embedding spaces are not designed to resemble humansemantic knowledge. Moreover, embeddings are often built from a single sourceof information (typically text data), even though neurocognitive researchsuggests that semantics is deeply linked to both language and perception. Inthis paper, we combine multimodal information from both text and image-basedrepresentations derived from state-of-the-art distributional models to producesparse, interpretable vectors using Joint Non-Negative Sparse Embedding.Through in-depth analyses comparing these sparse models to human-derivedbehavioural and neuroimaging data, we demonstrate their ability to predictinterpretable linguistic descriptions of human ground-truth semantic knowledge.

Using Sparse Semantic Embeddings Learned from Multimodal Text and Image Data to Model Human Conceptual Knowledge