Abstract
Language identification of written text has been studied for several decades. Despite this fact, most of the research is focused on a few most spoken languages, whereas the minor ones are ignored.
The identification of a larger number of languages brings new difficulties that do not occur for a few languages. These difficulties are causing decreased accuracy.
The objective of this paper is to investigate the sources of such degradation. In order to isolate the impact of individual factors, 5 different algorithms and 3 different number of languages are used.
The Support Vector Machine algorithm achieved an accuracy of 98% for 90 languages and the YALI algorithm based on a scoring function had an accuracy of 95.4%. The YALI algorithm has slightly lower accuracy but classifies around 17 times faster and its training is more than 4000 times faster.
Three different data sets with various number of languages and sample sizes were prepared to overcome the lack of standardized data sets. These data sets are n