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Transducer-learning experiments on language understanding

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Grammatical Inference (ICGI 1998)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1433))

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Abstract

The interest in using Finite-State Models in a large variety of applications is recently growing as more powerful techniques for learning them from examples have been developed. Language Understanding can be approached this way 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, and 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-level 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”.

Work partially supported by EuTrans, European Union ESPRIT LTR Project 30268, and the Spanish CICYT under grant TIC-0745-CO2.

Author supported by a FPI grant from the Conselleria d'Educació i Ciència of Valencian Government.

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Authors and Affiliations

  1. Institut TecnolÒgic d'Informàtica, Universitat Politècnica de València, València, Spain

    David Picó & Enrique Vidal

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  1. David Picó
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  2. Enrique Vidal
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Vasant Honavar Giora Slutzki

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© 1998 Springer-Verlag Berlin Heidelberg

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Picó, D., Vidal, E. (1998). Transducer-learning experiments on language understanding. In: Honavar, V., Slutzki, G. (eds) Grammatical Inference. ICGI 1998. Lecture Notes in Computer Science, vol 1433. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0054071

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  • DOI: https://doi.org/10.1007/BFb0054071

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  • Print ISBN: 978-3-540-64776-8

  • Online ISBN: 978-3-540-68707-8

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