We study a subclass of tree-to-word transducers: linear treeto- word transducers, that cannot use several copies of the input. We aim to study the equivalence problem on this class, by using minimization and normalization techniques. We identify a Myhill-Nerode characterization. It provides a minimal normal form on our class, computable in Exptime. This paper extends an already existing result on tree-to-word transducers without copy or reordering (sequential tree-to-word transducers), by accounting for all the possible reorderings in the output.
CITATION STYLE
Boiret, A. (2016). Normal form on linear tree-to-word transducers. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9618, pp. 439–451). Springer Verlag. https://doi.org/10.1007/978-3-319-30000-9_34
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