Modelling Parsing Constraints with High-dimensional Context Space

Abstract

Deriving representations of meaning has been a long-standing problem in cognitive psychology and psycholinguistics. The lack of a m odel for representing semantic and grammatical knowledge has been a handicap in attempting to model the effects of semantic constraints in hum an syntactic processing. A computational model of high-dim ensional context space, the Hyperspace A nalogue to Language (H AL), is presented with a series of simulations modelling a variety of human empirical results. HAL learns its representations from the unsupervised processing of 300 million words of conversational text. W e propose that HAL's high-dim ensional context space can be used to (1) provide a basic categorisation of semantic and grammatical concepts, (2) model certain aspects of morphological ambiguity in verbs, and (3) provide an account of semantic context effects in syntactic processing. W e propose that the distributed and contextually derived representations that HAL acquires provide a basis for the subconceptual knowledge that can be used in accounting for a diverse set of cognitive phenomena.