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Start date: 1.1.2010

End date: 31.12.2012

Key innovation

The PRESEMT project proposes a novel approach to the problem of Machine Translation by introducing cross-disciplinary techniques, mainly borrowed from the machine learning and computational intelligence domains, in the MT paradigm.

To this end, a flexible MT system will be developed, which will be enhanced with (a) pattern recognition approaches (such as extended clustering or neural networks) towards the development of a language-independent analysis and (b) evolutionary computation (such as Genetic Algorithms or Swarm Intelligence) for system optimisation.

 

Features

  1. Development of a novel method based on generalised clustering techniques, for creating a language-independent phrase aligner also adaptable to phrasing principles designated by the end users
  2.  
  3. Use of pattern recognition approaches for defining syntactic structure
  4. Employment of techniques inspired by the functional biological systems for disambiguating translations
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  6. Extensive use of automated optimisation techniques to define a mature system for methodically optimising system parameters
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  8. Application of machine learning methods for allowing system adaptation
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  10. Use of parallel computing architectures as well as mainstream multi-core architectures for PCs for substantial advances in translation speed
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