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Computational approaches in language identification often result in highnumber of false positivesand low recall rates, especially if the languages involved come from the same subfamily. In this paper,we aim to determine the cause of this problemby measuring language similarity through trigrams. Religious and literary texts were used as training data. Our experiments involving language identification show that the number of common trigrams for a given language pair is inversely proportional to precision and recall rates, whereas the average word length is directly proportional to the number of true positives. Future directions include improving language modeling and providing an approach to increase precision and recall.

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