Using a Machine Learning Model to Assess the Complexity of Stress Systems
Résumé
We address the task of stress prediction as a sequence tagging problem. We present sequential models with averaged perceptron training for learning primary stress in Romanian words. We use character n-grams and syllable n-grams as features and we account for the consonant-vowel structure of the words. We show in this paper that Romanian stress is predictable, though not deterministic, by using data-driven machine learning techniques.
Domaines
Linguistique| Origine | Fichiers produits par l'(les) auteur(s) |
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