Advances in Ngram-based Discrimination of Similar Languages

We describe the systems entered by the National Research Council in the 2016 shared task on discriminating similar languages. Like previous years, we relied on character ngram features, and a combination of discriminative and generative statistical classifiers. We mostly investigated the influence of the amount of data on the performance, in the open task, and compared the twostage approach (predicting language/group, then variant) to a flat approach. Results suggest that ngrams are still state-of-the-art for language and variant identification, that additional data has a small but decisive impact, and that the two-stage approach performs slightly better, everything else being kept equal, than the flat approach.