HAL (Hyperspace Analog to Language) is a high-dimensional model of semantic space that uses the global co-occurence frequency of words in a large corpus of text as the basis for its representation of semantic memory. We have explored the parameter space of the model to find an optimized set of parameters (such as window size, and weighting function) . These new parameters give us measures of semantic density that predict behavioral measures in human beings better than the original HAL parameters. |