@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://aclanthology.org/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.},
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="clematide-makarov-2017-cluzh">
<titleInfo>
<title>CLUZH at VarDial GDI 2017: Testing a Variety of Machine Learning Tools for the Classification of Swiss German Dialects</title>
</titleInfo>
<name type="personal">
<namePart type="given">Simon</namePart>
<namePart type="family">Clematide</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Peter</namePart>
<namePart type="family">Makarov</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2017-04</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Fourth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Preslav</namePart>
<namePart type="family">Nakov</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Marcos</namePart>
<namePart type="family">Zampieri</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nikola</namePart>
<namePart type="family">Ljubešić</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jörg</namePart>
<namePart type="family">Tiedemann</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Shevin</namePart>
<namePart type="family">Malmasi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ahmed</namePart>
<namePart type="family">Ali</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Valencia, Spain</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>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 non-neural approaches we did not include them in the submission.</abstract>
<identifier type="citekey">clematide-makarov-2017-cluzh</identifier>
<identifier type="doi">10.18653/v1/W17-1221</identifier>
<location>
<url>https://aclanthology.org/W17-1221</url>
</location>
<part>
<date>2017-04</date>
<extent unit="page">
<start>170</start>
<end>177</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T CLUZH at VarDial GDI 2017: Testing a Variety of Machine Learning Tools for the Classification of Swiss German Dialects
%A Clematide, Simon
%A Makarov, Peter
%Y Nakov, Preslav
%Y Zampieri, Marcos
%Y Ljubešić, Nikola
%Y Tiedemann, Jörg
%Y Malmasi, Shevin
%Y Ali, Ahmed
%S Proceedings of the Fourth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial)
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F clematide-makarov-2017-cluzh
%X 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 non-neural approaches we did not include them in the submission.
%R 10.18653/v1/W17-1221
%U https://aclanthology.org/W17-1221
%U https://doi.org/10.18653/v1/W17-1221
%P 170-177
Markdown (Informal)
[CLUZH at VarDial GDI 2017: Testing a Variety of Machine Learning Tools for the Classification of Swiss German Dialects](https://aclanthology.org/W17-1221) (Clematide & Makarov, VarDial 2017)
ACL