Nowadays, translation may be the bottleneck of the pretended
information globalisation. While surfing the Internet, for
instance, sometimes we come across languages and characters we
don't understand. Statistical machine translation (SMT) constitutes
a research sub-area of machine translation (MT) that has recently
gained much popularity. In fact, this technology has experienced
real growth motivated by the development of computer resources
needed to implement translation algorithms based on statistical
methods. This PhD thesis focuses on the SMT framework and primarly
on the definition and experimentation of novel algorithms for
building a correct structural reordering for translated words.
Moreover, challenging techniques regarding language modeling and
system combination are successfully applied to state-of-the-art SMT
systems. This dissertation should shed some light on the SMT
approach and on the word ordering challenges and should be
specially useful to natural language processing researchers having
non or some expertise in machine translation.
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