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* Corresponding author 1 LSE - Laboratoire des Sciences de l-Éducation Grenoble 2 AMA - Analyse de données, Modélisation et Apprentissage automatique Grenoble LIG - Laboratoire d-Informatique de Grenoble

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.

keyword : minimum description length computational cognitive model gender assignment





Autor: Harmony Marchal - Benoît Lemaire - Maryse Bianco - Philippe Dessus -

Fuente: https://hal.archives-ouvertes.fr/



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