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Smaller representations for finite-state transducers and finite-state automata

  • Emmanuel Roche
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 937)

Abstract

Finite-state transducers and finite-state automata are efficient and natural representations for a large variety of problems. We describe a new algorithm for turning a finite-state transducer into the composition of two deterministic finite-state transducers such that the combined size of the derived transducers can be exponentially smaller than other known deterministic constructions. As a consequence, this can also be used to build deterministic representations of finite-state automata smaller than the minimal finite-state automata computed by the classic determinization and minimization algorithms. We also report experimental results on large scale dictionaries and rule-based systems.

Keywords

Input Symbol Factorization Algorithm Deterministic Representation Graph Coloring Problem Deterministic Automaton 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Emmanuel Roche
    • 1
  1. 1.Mitsubishi Electric Research Laboratories 201Cambridge

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