Learning Sequential Tree-to-Word Transducers
Résumé
We study the problem of learning sequential top-down tree-to- word transducers (STWs). First, we present a Myhill-Nerode characterization of the corresponding class of sequential tree-to-word transformations (STW). Next, we investigate what learning of stws means, identify fundamental obstacles, and propose a learning model with abstain. Finally, we present a polynomial learning algorithm.
Domaines
Apprentissage [cs.LG]| Origine | Fichiers produits par l'(les) auteur(s) |
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