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Large Scale Inference of Deterministic Transductions: Tenjinno Problem 1

  • Alexander Clark
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4201)

Abstract

We discuss the problem of large scale grammatical inference in the context of the Tenjinno competition, with reference to the inference of deterministic finite state transducers, and discuss the design of the algorithms and the design and implementation of the program that solved the first problem. Though the OSTIA algorithm has good asymptotic guarantees for this class of problems, the amount of data required is prohibitive. We therefore developed a new strategy for inferring large scale transducers that is more adapted for large random instances of the type in question, which involved combining traditional state merging algorithms for inference of finite state automata with EM based alignment algorithms and state splitting algorithms.

Keywords

Machine Translation Expectation Maximisation Algorithm Input String State Automaton Input Language 
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 2006

Authors and Affiliations

  • Alexander Clark
    • 1
  1. 1.Department of Computer ScienceUniversity of LondonEgham, Surrey

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