Abstract
We investigate the performance of text-based language identification systems on the 11 official languages of South Africa, when n-gram statistics are used as features for classification. In particular, we compare support vector machines (SVMs) and likelihood-based classifiers on different amounts of input text, both from a closed domain and an open domain. With as few as 15 words of input text, reliable language identification is possible. Although the SVM is generally more accurate a classifier, the additional computational complexity of training this classifier may not be justified in light of the importance of using a large value for n.
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