%0 Conference Proceedings %T CLUZH at VarDial GDI 2017: Testing a Variety of Machine Learning Tools for the Classification of Swiss German Dialects %A Clematide, Simon %A Makarov, Peter %Y Nakov, Preslav %Y Zampieri, Marcos %Y Ljubešić, Nikola %Y Tiedemann, Jörg %Y Malmasi, Shevin %Y Ali, Ahmed %S Proceedings of the Fourth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial) %D 2017 %8 April %I Association for Computational Linguistics %C Valencia, Spain %F clematide-makarov-2017-cluzh %X Our submissions for the GDI 2017 Shared Task are the results from three different types of classifiers: Naïve Bayes, Conditional Random Fields (CRF), and Support Vector Machine (SVM). Our CRF-based run achieves a weighted F1 score of 65% (third rank) being beaten by the best system by 0.9%. Measured by classification accuracy, our ensemble run (Naïve Bayes, CRF, SVM) reaches 67% (second rank) being 1% lower than the best system. We also describe our experiments with Recurrent Neural Network (RNN) architectures. Since they performed worse than our non-neural approaches we did not include them in the submission. %R 10.18653/v1/W17-1221 %U https://aclanthology.org/W17-1221 %U https://doi.org/10.18653/v1/W17-1221 %P 170-177