Tuning Bayes Baseline for Dialect Detection

Hector-Hugo Franco-Penya, Liliana Mamani Sanchez


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.
Anthology ID:
W16-4829
Volume:
Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial3)
Month:
December
Year:
2016
Address:
Osaka, Japan
Venues:
VarDial | WS
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
227–234
Language:
URL:
https://aclanthology.org/W16-4829
DOI:
Bibkey:
Cite (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.
Cite (Informal):
Tuning Bayes Baseline for Dialect Detection (Franco-Penya & Mamani Sanchez, 2016)
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PDF:
https://aclanthology.org/W16-4829.pdf