  Parallel texts are a relatively rare language resource, however, they
constitute a very useful research material with a wide range of applications.
This study presents and analyses new methodologies we developed for obtaining
such data from previously built comparable corpora. The methodologies are
automatic and unsupervised which makes them good for large scale research. The
task is highly practical as non-parallel multilingual data occur much more
frequently than parallel corpora and accessing them is easy, although parallel
sentences are a considerably more useful resource. In this study, we propose a
method of automatic web crawling in order to build topic-aligned comparable
corpora, e.g. based on the Wikipedia or Euronews.com. We also developed new
methods of obtaining parallel sentences from comparable data and proposed
methods of filtration of corpora capable of selecting inconsistent or only
partially equivalent translations. Our methods are easily scalable to other
languages. Evaluation of the quality of the created corpora was performed by
analysing the impact of their use on statistical machine translation systems.
Experiments were presented on the basis of the Polish-English language pair for
texts from different domains, i.e. lectures, phrasebooks, film dialogues,
European Parliament proceedings and texts contained medicines leaflets. We also
tested a second method of creating parallel corpora based on data from
comparable corpora which allows for automatically expanding the existing corpus
of sentences about a given domain on the basis of analogies found between them.
It does not require, therefore, having past parallel resources in order to
train a classifier.
