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