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Using Sparse Semantic Embeddings Learned from Multimodal Text and Image Data to Model Human Conceptual Knowledge
Distributional models provide a convenient way to model semantics using dense
embedding spaces derived from unsupervised learning algorithms. However, the
dimensions of dense embedding spaces are not designed to resemble human
semantic knowledge. Moreover, embeddings are often built from a single source
of information (typically text data), even though neurocognitive research
suggests that semantics is deeply linked to both language and perception. In
this paper, we combine multimodal information from both text and image-based
representations derived from state-of-the-art distributional models to produce
sparse, interpretable vectors using Joint Non-Negative Sparse Embedding.
Through in-depth analyses comparing these sparse models to human-derived
behavioural and neuroimaging data, we demonstrate their ability to predict
interpretable linguistic descriptions of human ground-truth semantic knowledge.Comment: Proceedings of the 22nd Conference on Computational Natural Language
Learning (CoNLL 2018), pages 260-270. Brussels, Belgium, October 31 -
November 1, 2018. Association for Computational Linguistic
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