We present a Bayesian model of the mirror image problems of linguistic productivity and reuse.The model, known as Fragment Grammar, is evaluated against several morphological datasets; its performance is compared to competing theoretical accounts including full-parsing, full-listing, and exemplar-based models.The model is able to learn the correct patterns of productivity and reuse for two very different systems: the English past tense which is characterized by a sharp dichotomy in productivity between regular and irregular forms and English derivational morphology which is characterized by a graded cline from very productive (-ness) to very unproductive (-th).
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