In this paper, we revisit the problem of language identification with the focus on proper discrimination between closely related languages. Strong similarities between certain languages make it very hard to classify them correctly using standard methods that have been proposed in the literature. Dedicated models that focus on specific discrimination tasks help to improve the accuracy of general-purpose language identification tools. We propose and compare methods based on simple document classification techniques trained on parallel corpora of closely related languages and methods that emphasize discriminating features in terms of blacklisted words. Our experiments demonstrate that these techniques are highly accurate for the difficult task of discriminating between Bosnian, Croatian and Serbian. The best setup yields an absolute improvement of over 9% in accuracy over the best performing baseline using a state-of-the-art language identification tool.
REF improved this method and applied it to Bosnian, Croatian and Serbian texts.
Efficient Discrimination Between Closely Related Languages
COLING
Computer Science