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Abstract

David Picó, Enrique Vidal. Learning Finite-State Models for Language Understanding. Proceedings of the International Workshop on Finite State Methods in Natural Language Processing, 1998. pp. 69-78. Bilkent University, Ankara, Turkey.

Language Understanding in limited domains is here approached as a problem of language translation in which the target language is a formal language rather than a natural one. Finite-state transducers are used to model the translation process. Furthermore, these models are automatically learned from training data consisting of pairs of natural-language/formal-language sentences. The need for training data is dramatically reduced by performing a two-step learning process based on lexical/phrase categorization. Successful experiments are presented on a task consisting in the "understanding" of Spanish natural-language sentences describing dates and times, where the target formal language is the one used in the popular Unix command "at".