Pattern Recognition-based Statistically Enhanced MT (PRESEMT)
- Project Identification
- Project Period
- 1/2010 - 12/2012
- Investor / Pogramme / Project type
- European Union
- MU Faculty or unit
- Faculty of Informatics
- Cooperating Organization
Institute for Language and Speech Processing
Gesellschaft zurFörderung angewandter Informatik
Norwegian University of Science and Technology
National Technical University of Athens
Lexical Computing Ltd.
This proposal describes PRESEMT, a flexible and adaptable MT system, based on a language-independent method, whose principles ensure easy portability to new language pairs. This method attempts to overcome well-known problems of other MT approaches, e.g. bilingual corpora compilation or creation of new rules per language pair. PRESEMT will address the issue of effectively managing multilingual content and is expected to suggest a language-independent machine-learning-based methodology. The key aspects of PRESEMT involve syntactic phrase-based modelling, pattern recognition approaches (such as extended clustering or neural networks) or game theory techniques towards the development of a language-independent analysis, evolutionary algorithms for system optimisation. It is intended to be of a hybrid nature, combining linguistic processing with the positive aspects of corpus-based approaches, such as SMT and EBMT.
Total number of publications: 14