@inproceedings{clematide-makarov-2017-cluzh,
    title = "{CLUZH} at {V}ar{D}ial {GDI} 2017: Testing a Variety of Machine Learning Tools for the Classification of {S}wiss {G}erman Dialects",
    author = "Clematide, Simon  and
      Makarov, Peter",
    editor = {Nakov, Preslav  and
      Zampieri, Marcos  and
      Ljube{\v{s}}i{\'c}, Nikola  and
      Tiedemann, J{\"o}rg  and
      Malmasi, Shevin  and
      Ali, Ahmed},
    booktitle = "Proceedings of the Fourth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/W17-1221/",
    doi = "10.18653/v1/W17-1221",
    pages = "170--177",
    abstract = {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.}
}