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Describes a computational model of high-dimensional context space: the Hyperspace Analog to Language (HAL). Shows that HAL provides sufficient information to make semantic, grammatical, and abstract distinctions. Demonstrates the cognitive compatibility of the representations with human processing; and introduces a new methodology that extracts conceptual intersections from the high-dimensional context space. (SR)

Presents a model of high-dimensional context space, the Hyperspace Analogue to Language (HAL), with a series of simulations modelling human empirical results. Proposes that HAL's context space can be used to provide a basic categorization of semantic and grammatical concepts; model certain aspects of morphological ambiguity in verbs; and provide an account of semantic context effects in syntactic processing. (77 references) (Author/CK)

Presents a procedure that processes a corpus of text and produces numeric vectors containing information about its meanings for each word. Application of the procedure to a corpus of natural language taken from Usenet; Analysis of resulting vectors; Analyses of vector similarity and multidimensional scaling.