@InProceedings{clematide-makarov:2017:VarDial,
  author    = {Clematide, Simon  and  Makarov, Peter},
  title     = {CLUZH at VarDial GDI 2017: Testing a Variety of Machine Learning Tools for the Classification of Swiss German Dialects},
  booktitle = {Proceedings of the Fourth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial)},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {170--177},
  abstract  = {Our submissions for the GDI 2017 Shared Task are the results from three
	different types of classifiers: Naı̈ve 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 (Naı̈ve 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.},
  url       = {http://www.aclweb.org/anthology/W17-1221}
}