HiDEx

HAL (Hyperspace Analog to Language) is a high-dimensional model of semantic space that uses the global co-occurrence frequency of words in a large corpus of text as the basis for a representation of semantic memory. In the original HAL model, many parameters were set without any a priori rationale. In this chapter we describe a new computer application called the High Dimensional Explorer (HiDEx) that makes it possible to systematically alter the values of the model’s parameters and thereby to examine their effect on the co-occurrence matrix that instantiates the model. New parameter sets give us measures of semantic density that improve the model’s ability to predict behavioral measures. Implications for such models are discussed.