Normalization of Sequential Top-Down Tree-to-Word Transducers

Laurence, G; Lemay, Aurélien; Niehren, Joachim; Staworko, Sławek; Tommasi, Marc

doi:10.1007/978-3-642-21254-3_28

Cited by 10 publications

(33 citation statements)

References 14 publications

(25 reference statements)

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“…This idea initially comes from normalisation of word-to-word transducers, as in [4], and is also employed in [13,9] for tree-to-tree transducers. In [12], we have proposed the following normal form for stws.…”

Section: Sequential Top-down Tree-to-word Transducersmentioning

confidence: 99%

“…Following the work of [12], we study the class of deterministic sequential top-down tree-to-word transducers (stws). stws are finite state machines that traverse the input tree in top-down fashion and at every node produce words obtained by the concatenation of constant words and the results from processing the child nodes.…”

Section: Introductionmentioning

confidence: 99%

“…These features are often missing in tree-to-tree transducers, and for instance, make stws incomparable with the class of top-down tree-totree transducers [7,13]. The class of stws has several interesting properties, in particular a normal form has been proposed in [12].…”

Section: Introductionmentioning

confidence: 99%

“…An open question raised in [12] was the existence of a Myhill-Nerode Theorem for stws. We solve this question and this result is the first main contribution of this paper.…”

Section: Introductionmentioning

confidence: 99%

“…The Myhill-Nerode Theorem proof starts from the identification of the class of earliest stws (estws) given in [12]. The main difficulty is to be able to characterize residual languages of a stw transformation and then define a canonical representative for estws.…”

Section: Introductionmentioning

confidence: 99%

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Learning Sequential Tree-to-Word Transducers

Laurence

Lemay

Niehren

et al. 2014

Language and Automata Theory and Applications

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Abstract. We study the problem of learning sequential top-down tree-toword transducers (stws). First, we present a Myhill-Nerode characterization of the corresponding class of sequential tree-to-word transformations (ST W). 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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Section: Sequential Top-down Tree-to-word Transducersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…An open question raised in [12] was the existence of a Myhill-Nerode Theorem for stws. We solve this question and this result is the first main contribution of this paper.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Learning Sequential Tree-to-Word Transducers

Laurence

Lemay

Niehren

et al. 2014

Language and Automata Theory and Applications

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Deciding Equivalence of Separated Non-nested Attribute Systems in Polynomial Time

Seidl

Palenta

Maneth

2019

Lecture Notes in Computer Science

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In 1982, Courcelle and Franchi-Zannettacci showed that the equivalence problem of separated non-nested attribute systems can be reduced to the equivalence problem of total deterministic separated basic macro tree transducers. They also gave a procedure for deciding equivalence of transducer in the latter class. Here, we reconsider this equivalence problem. We present a new alternative decision procedure and prove that it runs in polynomial time. We also consider extensions of this result to partial transducers and to the case where parameters of transducers accumulate strings instead of trees.

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