We report experimental results in AA data sets that confirm that LHs over character n-grams are more helpful for AA than the usual global histograms, yielding results far superior to state of the art approaches.

Our experimental results showed that LHs outperform traditional bag-of-words formulations and state of the art techniques in balanced, imbalanced, and reduced data sets.

The BOLH formulation outperforms state of the art approaches by a considerable margin that ranges from 10% to 27%.

c We report experimental results that are superior to state of the art approaches (Plakias and Stamatatos, 2008b; Plakias and Stamatatos, 2008a), with improvements ranging from 2% — 6% in balanced data sets and from 14% — 30% in imbalanced data sets.

Using our proposed approach we obtain better scores than the state of the art on the English-Finnish translation task (Luong et al., 2010): from 14.82% BLEU to 15.09%, while using a

Both of these approaches beat the state of the art on the English-Finnish translation task.

Our proposed approaches are significantly better than the state of the art , achieving the highest reported BLEU scores on the English-Finnish Europarl version 3 dataset.