Start date: 1.1.2010
End date: 31.12.2012
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.
- 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
- Use of pattern recognition approaches for defining syntactic structure
- Employment of techniques inspired by the functional biological systems for disambiguating translations
- Extensive use of automated optimisation techniques to define a mature system for methodically optimising system parameters
- Application of machine learning methods for allowing system adaptation
- Use of parallel computing architectures as well as mainstream multi-core architectures for PCs for substantial advances in translation speed