Proceedings of the Annual Meeting of the Cognitive Science Society

Recent work on representation in simple recursive feed forward connecdonist networks suggests that a computational device can learn linguistic behaviors without any explicit representation of linguistic knowledge in the form of rules, facts, or procedures. This paper presents an extension of these methods to the study of lexical ambiguity resolution and semantic parsing. Five specific hypotheses are discussed regarding network architectures for lexical ambiguity resolution and the nature of their performance: (1) A simple recurrent feed forward network using back propagation can learn to predict correctly the object of ambiguous verb "take out" in specific contexts; (2) Such a network can likewise predict a pronoun of the correct gender in the appropriate contexts; (3) The effect of specific contextual features increases with their proximity to the ambiguous word or words; (4) The training of hidden recurrent networks for lexical ambiguity resolution improves significantly when the input consists of two words rather than a single word; and (5) The principal components of the hidden units in the trained networks reflect an internal representation of linguistic knowledge. Experimental results supporting these hypotheses are presented, including analysis of network performance and acquired representations. The paper concludes with a discussion of the work in terms of computational neuropsychology, with potential impact on clinical and basic neuroscience.