We present a method for improving existing statistical machine trans-
lation methods using a knowledge base compiled from a bilingual corpus as well
as sequence alignment and pattern matching techniques from the area of machine
learning and bioinformatics. An alignment algorithm identifies similar sentences,
which are then used to construct a better word order for the translation. Our pre-
liminary test results indicate a significant improvement of the translation quality.
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