We describe a model of natural language understanding based on the notion of propagating constraints in a semantic memory. This model contains a massively-parallel memory-network in which constraint graphs that represent syntactic and other constraints that are associated awith the nodes that triggered activations are propagated. The propagated constraint graphs of complement nodes that coUide with the constraint graphs postulated by the head nodes are unified to perform constraint applications. This mechanism handles linguistic phenomena such as case, agreement, binding and control in a principled manner in effect equivalent to the manner that they are handled in modern linguistic theories.

We will describe a model of natural language understanding based on Head-driven Massively-parallel Constraint Propagation (HMCP). This model contains a massively parallel memory network in which syntactic head-features are propagated along with other information concerning the nodes that triggered the propagation. The propagated head features eventually collide with subcategorization lists which contain constraints on subcategorized arguments.These mechanisms handle linguistic phenomena such as case, agreement, complement order, and control which are fundamental to linguistic analysis but have not been captured in previous marker-passing models.