A major project has been the development of the Hyperspace Analogue to Language (or HAL), which is a computer simulation of human memory. HAL has a lexicon of 70,000 items and learns its representations as a function of the contexts in which words occur. This is accomplished with a concept-acquisition process that requires no supervision using an input of 320 million words of text. Word meanings (broadly based) are represented in a 140,000 dimensional space (thus, Hyperspace Analogue to Language). The model accounts for a wide range of semantic, language, grammatical, and syntactic phenomena. New areas of exploration for the model involve commercial and forensic applications as well as memory disorders in deep dyslexia, schizophrenia, AlzheimerÕs and normal aging.

Our most recent work involves incorporating the HAL semantic representations into a connectionist style processing model. In this way we can begin to explore the interaction between processing and representational issues.