  The multilingual nature of the world makes translation a crucial requirement
today. Parallel dictionaries constructed by humans are a widely-available
resource, but they are limited and do not provide enough coverage for good
quality translation purposes, due to out-of-vocabulary words and neologisms.
This motivates the use of statistical translation systems, which are
unfortunately dependent on the quantity and quality of training data. Such
systems have a very limited availability especially for some languages and very
narrow text domains. In this research we present our improvements to current
comparable corpora mining methodologies by re- implementation of the comparison
algorithms (using Needleman-Wunch algorithm), introduction of a tuning script
and computation time improvement by GPU acceleration. Experiments are carried
out on bilingual data extracted from the Wikipedia, on various domains. For the
Wikipedia itself, additional cross-lingual comparison heuristics were
introduced. The modifications made a positive impact on the quality and
quantity of mined data and on the translation quality.
