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Deriving representations of meaning, whether at the word, the sentence, or the discourse level, is a problem with a long history in cognitive psychology and psycholinguistics. In this article, we describe a computational model of high-dimensional context space, the Hyperspace Analog to Language (HAL). and present simulation evidence that HAL's vector representations can provide sufficient information to make semantic, grammatical, and abstract distinctions. Human participants were able to use the context neighborhoods that HAL generates to match words with similar items and to derive the word for a similar word) from the neighborhood, thus demonstrating the cognitive compatibility of the representations with human processing. An experiment exploring the meaning of agent- and patient-oriented verbs provided the context for discussing how the connotative aspects of word neighborhoods could provide cues in establishing a discourse model. Using the vector representations to build a sentence-level representation was attempted, but the results were not promising. This limitation of the representations is presented in the context of other similar models that have been more effective. A final set of experiments explored the nature of verbs and verb instruments, and we introduce a new methodology that extracts conceptual intersections from the high-dimensional context space. [ABSTRACT FROM AUTHOR]

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.

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. [ABSTRACT FROM AUTHOR]

Systolic (SP), diastolic (DP), and mean arterial blood pressures (MAP) and pulse rate (PR) were recorded on treated and untreated conscious cynomolgus monkeys by the oscillometric method (Dinamap 1846SX/P). Each monkey was placed in a restraining tube with the cuff placed on the base on the shaved tail. Measurements were taken on untreated animals once or twice a day for 4 weeks. The mean and standard deviations for SP, DP, MAP, and PR were approximately 121 ± 17, 60 ± 14, and 84 ± 17 mm Hg and 193 ± 18 pulses/min, respectively. One male and one female cynomolgus monkey were treated with isoproterenol, norepinephrine, and nitropmsside. Blood pressure was measured indirectly with the cuff on the tail and directly with an indwelling catheter in the descending thoracic aorta. Although the oscillometric method was not as sensitive as the catheter, the oscillometric method detected a change in the same direction of SP, DP, MAP, and PR compared with the direct method for all drugs administered. The monitor was reliable and sufficiently accurate to conclude that it may be useful in toxicology studies for evaluation of blood pressure parameters in Conscious Cynomolgus monkeys. [ABSTRACT FROM PUBLISHER]