Naturalistic Word-Concept Pair Learning With Semantic Spaces


We describe a model designed to learn word-concept pairings using a combination of semantic space models. We compare various semantic space models to each other as well as to extant word-learning models in the literature and find that not only do semantic space models require fewer underlying assumptions, they perform at least on par with existing associative models. We also demonstrate that semantic space models correctly predict different word-concept pairings from existing models and can be combined with existing models to perform better than either model can individually.

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