Abstract. We study normalization of deterministic sequential top-down tree-to-word transducers (stws), that capture the class of deterministic top-down nested-word to word transducers. We identify the subclass of earliest stws (estws) that yield normal forms when minimized. The main result of this paper is an effective normalization procedure for stws. It consists of two stages: we first convert a given stw to an equivalent estw, and then, we minimize the estw.
Abstract. We study the equivalence problem of deterministic nested word to word transducers and show it to be surprisingly robust. Modulo polynomial time reductions, it can be identified with 4 equivalence problems for diverse classes of deterministic non-copying order-preserving transducers. In particular, we present polynomial time back and fourth reductions to the morphism equivalence problem on context free languages, which is known to be solvable in polynomial time.
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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