Abstract
This paper presents a theoretical approach of how simple, episodic associations are transduced into semantic and grammatical categorical knowledge. The approach is implemented in the hyperspace analogue to language (HAL) model of memory, which uses a simple global co-occurrence learning algorithm to encode the context in which words occur. This encoding is the basis for the formation of meaning representations in a high-dimensional context space. Results are presented, and the argument is made that this simple process can ultimately provide the language-comprehension system with semantic and grammatical information required in the comprehension process.
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This research was supported by NSF Presidential Faculty Fellow Award SBR-9453406 to the author. A version of this paper was presented as the keynote talk at the 1997 Society for Computers in Psychology (SCiP) annual meeting in Philadelphia. Kevin Lund, Catherine Decker, and two anonymous reviewers provided many helpful comments. More information about research at the Computational Cognition Lab, a HAL demo, and reprint information can be found at http://HAL.ucr.edu.
An erratum to this article is available at http://dx.doi.org/10.3758/BF03200689.
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Burgess, C. From simple associations to the building blocks of language: Modeling meaning in memory with the HAL model. Behavior Research Methods, Instruments, & Computers 30, 188–198 (1998). https://doi.org/10.3758/BF03200643
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DOI: https://doi.org/10.3758/BF03200643