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Direct linkJones, Michael N.; Gruenenfelder, Thomas M.; Recchia, Gabriel – Grantee Submission, 2017
Recent semantic space models learn vector representations for word meanings by observing statistical redundancies across a text corpus. A word's meaning is represented as a point in a high-dimensional semantic space, and semantic similarity between words is quantified by a function of their spatial proximity (typically the cosine of the angle…
Descriptors: Semantics, Computational Linguistics, Spatial Ability, Proximity
