A temporal connectionist approach to natural language

Abstract

Time is an essential dimension of human natural language understanding but most of the symbolic models applied to linguistic data do not account for temporal structure. In contrast, the models from the connectionist paradigm have a natural ability to perform dynamic processing.After a presentation of some networks with a concern for time, we describe the model for Coincidence Detection which can be thought of as encoding spatio-temporal regularities of the input data. The architecture of the model is inspired from neurobiological studies of the cerebral cortex. It performs a dynamic interpretation of nominal composition and is analyzed in terms of micro-symbolic co-occurrences. The relevance of the Coincidence Detection machinery in language processing shows the significance of time in computational language understanding.