Abstract
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 (\({\mathcal{STW}}\)). 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.
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Laurence, G., Lemay, A., Niehren, J., Staworko, S., Tommasi, M. (2014). Learning Sequential Tree-to-Word Transducers. In: Dediu, AH., Martín-Vide, C., Sierra-Rodríguez, JL., Truthe, B. (eds) Language and Automata Theory and Applications. LATA 2014. Lecture Notes in Computer Science, vol 8370. Springer, Cham. https://doi.org/10.1007/978-3-319-04921-2_40
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DOI: https://doi.org/10.1007/978-3-319-04921-2_40
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