CLUZH at VarDial GDI 2017: Testing a Variety of Machine Learning Tools for the Classification of Swiss German Dialects

Abstract

Our submissions for the GDI 2017 Shared Task are the results from three different types of classifiers: Na\" ve 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 (Na\" ve 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.

Tasks

Reproductions