Proceedings of the Annual Meeting of the Cognitive Science Society

We present a computational model of the acquisition of German case that is evaluated against empirical data obtained from naturalistic speech. The model substitutes nouns into existing contexts, and proceeds through a number of stages that reflect increasing knowledge on the part of a child, both of the determiner-noun sequences that are legal in German, and of the determiner-noun sequences that are appropriate in specific sentential contexts (cases). The model provides a natural account of gender and case errors, the two most common error types produced by children, and shows the highest error rates in dative contexts and lowest error rates in nominative contexts, as is true of children learning German. However, the model’s error rates in the early stages are considerably higher than those shown by children, suggesting that children possess a fairly sophisticated representation of how lexical contexts assign case from a relatively early age.