Proceedings of the Annual Meeting of the Cognitive Science Society

We have proposed a neural network style model of language processing in a na effort to build a cognitive model which would simultaneously satisfy constraints from psychology and neurophysiology. This model was successful in disambiguating word senses in semantically determined sentences, but was unable to distinguish Agent from Object in semantically reversible sentences such as "John loves Mary." In this paper we rectify the matter by specifying the syntactic portion of the model, which is a massively parallel, completely distributed connectionist parser. We also describe the results of a simulation of the model.