CLUZH at VarDial GDI 2017: Testing a Variety of Machine Learning Tools for the Classification of Swiss German Dialects
Our submissions for the GDI 2017 Shared Task are the results from three different types of classifiers: Na{\"\i}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{\"\i}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.
PDF Abstract
Datasets
Add Datasets
introduced or used in this paper
Results from the Paper
Submit
results from this paper
to get state-of-the-art GitHub badges and help the
community compare results to other papers.
