A MDL-based model of gender knowledge acquisition

Abstract

This paper presents an iterative model of knowledge acquisition of gender information associated with word endings in French. Gender knowledge is represented as a set of rules containing exceptions. Our model takes noun-gender pairs as input and constantly maintains a list of rules and exceptions which is both coherent with the input data and minimal with respect to a minimum description length criterion. This model was compared to human data at various ages and showed a good fit. We also compared the kind of rules discovered by the model with rules usually extracted by linguists and found interesting discrepancies.