Sequential Tree-to-Word Transducers: Normalization, Minimization, and Learning

Grégoire Laurence 1 Aurélien Lemay 1 Joachim Niehren 1 Slawomir Staworko 1 Marc Tommasi 2
1 LINKS - Linking Dynamic Data
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
2 MAGNET - Machine Learning in Information Networks
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Abstract : We introduce a class of deterministic sequential top-down tree-to-word transduc- ers (STWs) and investigate a number of its fundamental properties and related problems. First, we investigate the problem of normalization of STWs: we identify a subclass of earliest STWs (eSTWs) that is as expressive as stws and present an effective procedure for converting an arbitrary STW into an equivalent eSTW. We then present a Myhill-Nerode characterization of the class of the transformations definable with STWs which also shows that every transformation defined with an stw has a unique canonical representative eSTW. This canonical eSTW is the minimal eSTW defining the same transformation, and consequently, we present a polynomial minimization procedure for eSTWs, thus giving an effective procedure for constructing the canonical representative of any transformation definable with STWs. Finally, we use the Myhill-Nerode characterization to devise an algorithm for inference (learning) of eSTWs from examples of transformation given by the user.

Available at http://chercheurs.lille.inria.fr/~niehren/learning-stw/0.pdf

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https://hal.archives-ouvertes.fr/hal-01186993
Contributor : Inria Links <>
Submitted on : Tuesday, August 25, 2015 - 6:22:07 PM
Last modification on : Friday, March 22, 2019 - 1:34:56 AM

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  • HAL Id : hal-01186993, version 1

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Grégoire Laurence, Aurélien Lemay, Joachim Niehren, Slawomir Staworko, Marc Tommasi. Sequential Tree-to-Word Transducers: Normalization, Minimization, and Learning. 2015. ⟨hal-01186993⟩

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