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Conference Papers Year : 2014

Learning Sequential Tree-to-Word Transducers

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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 (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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Dates and versions

hal-00912969 , version 1 (03-12-2013)

Identifiers

  • HAL Id : hal-00912969 , version 1

Cite

Grégoire Laurence, Aurélien Lemay, Joachim Niehren, Slawomir Staworko, Marc Tommasi. Learning Sequential Tree-to-Word Transducers. 8th International Conference on Language and Automata Theory and Applications, Mar 2014, Madrid, Spain. ⟨hal-00912969⟩
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