CLUZH at VarDial GDI 2017: Testing a Variety of Machine Learning Tools for the Classification of Swiss German Dialects
Simon Clematide, Peter Makarov · 2017
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 nonneural approaches we did not include them in the submission.