A Variant of N-Gram Based Language Classification

Rapid classification of documents is of high-importance in many multilingual settings (such as international institutions or Internet search engines). This has been, for years, a well-known problem, addressed by different techniques, with excellent results. We address this problem by a simple n-grams based technique, a variation of techniques of this family. Our n-grams-based classification is very robust and successful, even for 20-fold classification, and even for short text strings. We give a detailed study for different lengths of strings and size of n-grams and we explore what classification parameters give the best performance. There is no requirement for vocabularies, but only for a few training documents. As a main corpus, we used a EU set of documents in 20 languages. Experimental comparison shows that our approach gives better results than four other popular approaches.