Using bag-of-words to distinguish similar languages: How efficient are they?

This paper presents a number of experiments describing the use of machine learning algorithms and bag-of-words to the task of automatic language identification. The paper focuses on the identification of language varieties, which is a known weakness of general purpose language identification methods. This question was addressed by a number of studies in the recent years, most of them relying on character n-gram language models. In this paper, I experiment simple bag-of-words and compare the results with previously proposed n-gram-based approaches. To perform these classification experiments three algorithms were used: Multinomial Naive Bayes (MNB), Support Vector Machines (SVM) and the J48 classifier.