Brent Kievit-Kylar

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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(More)
Semantic models play an important role in cognitive science. These models use statistical learning to model word meanings from co-occurrences in text corpora. A wide variety of semantic models have been proposed, and the literature has typically emphasized situations in which one model outperforms another. However, because these models often vary with(More)
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