@inproceedings{franco-penya-mamani-sanchez-2016-tuning,
title = "Tuning {B}ayes Baseline for Dialect Detection",
author = "Franco-Penya, Hector-Hugo and
Mamani Sanchez, Liliana",
editor = {Nakov, Preslav and
Zampieri, Marcos and
Tan, Liling and
Ljube\v si\'c, Nikola and
Tiedemann, J\"org and
Malmasi, Shervin},
booktitle = "Proceedings of the Third Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial3)",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/W16-4829/",
pages = "227--234",
abstract = "This paper describes an analysis of our submissions to the Dialect Detection Shared Task 2016. We proposed three different systems that involved simplistic features, to name: a Naive-bayes system, a Support Vector Machines-based system and a Tree Kernel-based system. These systems underperform when compared to other submissions in this shared task, since the best one achieved an accuracy of \textasciitilde 0.834."
}
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<abstract>This paper describes an analysis of our submissions to the Dialect Detection Shared Task 2016. We proposed three different systems that involved simplistic features, to name: a Naive-bayes system, a Support Vector Machines-based system and a Tree Kernel-based system. These systems underperform when compared to other submissions in this shared task, since the best one achieved an accuracy of ~ 0.834.</abstract>
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%0 Conference Proceedings
%T Tuning Bayes Baseline for Dialect Detection
%A Franco-Penya, Hector-Hugo
%A Mamani Sanchez, Liliana
%Y Nakov, Preslav
%Y Zampieri, Marcos
%Y Tan, Liling
%Y Ljubešić, Nikola
%Y Tiedemann, Jörg
%Y Malmasi, Shervin
%S Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial3)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F franco-penya-mamani-sanchez-2016-tuning
%X This paper describes an analysis of our submissions to the Dialect Detection Shared Task 2016. We proposed three different systems that involved simplistic features, to name: a Naive-bayes system, a Support Vector Machines-based system and a Tree Kernel-based system. These systems underperform when compared to other submissions in this shared task, since the best one achieved an accuracy of ~ 0.834.
%U https://aclanthology.org/W16-4829/
%P 227-234
Markdown (Informal)
[Tuning Bayes Baseline for Dialect Detection](https://aclanthology.org/W16-4829/) (Franco-Penya & Mamani Sanchez, VarDial 2016)
ACL
- Hector-Hugo Franco-Penya and Liliana Mamani Sanchez. 2016. Tuning Bayes Baseline for Dialect Detection. In Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial3), pages 227–234, Osaka, Japan. The COLING 2016 Organizing Committee.