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Maximum mutual information and conditional maximum likelihood estimations of stochastic regular syntax-directed translation schemes

  • Session: Inference of Stochastic Models 2
  • Conference paper
  • First Online:
Grammatical Interference: Learning Syntax from Sentences (ICGI 1996)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1147))

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Abstract

Formal translations have become of great interest for modeling some Pattern Recognition problems, but they require a stochastic extension in order to deal with noisy and distorted patterns. A Maximum Likelihood estimation has been recently developed for learning the statistical parameters of Stochastic Regular Syntax-Directed Translation Schemes. The goal of this paper is the study of estimation criteria in order to take into account the problem of sparse training data. In particular, these are the Maximum Mutual Information criterion and the Conditional Maximum Likelihood criterion. Some experimental results are reported to compare the three criteria.

Work partially supported by the Spanish CICYT under grant TIC95-0884-C04-01

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Authors and Affiliations

  1. Departamento de Sistemas Informáticos y Computación, Universidad Politécnica de Valencia, Spain

    F. Casacuberta

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  1. F. Casacuberta
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Laurent Miclet Colin de la Higuera

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© 1996 Springer-Verlag Berlin Heidelberg

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Casacuberta, F. (1996). Maximum mutual information and conditional maximum likelihood estimations of stochastic regular syntax-directed translation schemes. In: Miclet, L., de la Higuera, C. (eds) Grammatical Interference: Learning Syntax from Sentences. ICGI 1996. Lecture Notes in Computer Science, vol 1147. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0033362

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  • DOI: https://doi.org/10.1007/BFb0033362

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61778-5

  • Online ISBN: 978-3-540-70678-6

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