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Abstract. We present a reflection on the evolution of the different methods for constructing minimal deterministic acyclic finite-state automata from a finite set of words. We outline the most important methods, including the traditional ones (which consist of the combination of two phases: insertion of words and minimization of the partial automaton) and the incremental algorithms (which add new words one by one and minimize the resulting automaton on-the-fly, being much faster and having significantly lower memory requirements). We analyze their main features in order to provide some improvements for incremental constructions, and a general architecture that is needed to implement large dictionaries in natural language processing (NLP) applications.

Abstract. This article presents two new approaches for term indexing which are particularly appropriate for languages with a rich lexis and morphology, such as Spanish, and need few resources to be applied. At word level, productive derivational morphology is used to conflate semantically related words. At sentence level, an approximate grammar is used to conflate syntactic and morphosyntactic variants of a given multi-word term into a common base form. Experimental results show remarkable improvements with regard to classical indexing methods.

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Abstract. We describe a proposal on spelling correction intended to be applied on Galician, a Romance language. Our aim is to put into evidence the flexibility of a novelty technique that provides a quality equivalent to global strategies, but with a significantly minor computational cost. To do it, we take advantage of the grammatical background present in the recognizer, which allows us to dynamically gather information to the right and to the left of the point at which the recognition halts in a word, as long as this information could be considered as relevant for the repair process. The experimental tests prove the validity of our approach in relation to previous ones, focusing on both performance and costs.