Learning with weighted transducers

Corinna Cortes, Mehryar Mohri

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Weighted finite-state transducers have been used successfully in a variety of natural language processing applications, including speech recognition, speech synthesis, and machine translation. This paper shows how weighted transducers can be combined with existing learning algorithms to form powerful techniques for sequence learning problems.

Original languageEnglish (US)
Title of host publicationFinite-State Methods and Natural Language Processing
PublisherIOS Press
Number of pages9
ISBN (Print)9781586039752
StatePublished - 2009

Publication series

NameFrontiers in Artificial Intelligence and Applications
ISSN (Print)0922-6389


  • Classification
  • Clustering
  • Kernels
  • Learning
  • Ranking
  • Rational powers series
  • Regression
  • Weighted automata
  • Weighted transducers

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Cortes, C., & Mohri, M. (2009). Learning with weighted transducers. In Finite-State Methods and Natural Language Processing (1 ed., pp. 14-22). (Frontiers in Artificial Intelligence and Applications; Vol. 191, No. 1). IOS Press. https://doi.org/10.3233/978-1-58603-975-2-14