In this paper we describe the language identification system we developed for the Discriminating Similar Languages (DSL) 2015 shared task. We constructed a classifier ensemble composed of several Support Vector Machine (SVM) base classifiers, each trained on a single feature type. Our feature types include character 1-6 grams and word unigrams and bigrams. Using this system we were able to outperform the other entries in the closed training track of the DSL 2015 shared task, achieving the best accuracy of 95.54%.
At this shared task, the best accuracy was 95.54% and was achieved by REF .
Language identification using classifier ensembles
Computer Science