@inproceedings{guggilla-2016-discrimination,
title = "Discrimination between Similar Languages, Varieties and Dialects using {CNN}- and {LSTM}-based Deep Neural Networks",
author = "Guggilla, Chinnappa",
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/W16-4824",
pages = "185--194",
abstract = "In this paper, we describe a system (CGLI) for discriminating similar languages, varieties and dialects using convolutional neural networks (CNNs) and long short-term memory (LSTM) neural networks. We have participated in the Arabic dialect identification sub-task of DSL 2016 shared task for distinguishing different Arabic language texts under closed submission track. Our proposed approach is language independent and works for discriminating any given set of languages, varieties, and dialects. We have obtained 43.29{\%} weighted-F1 accuracy in this sub-task using CNN approach using default network parameters.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="guggilla-2016-discrimination">
<titleInfo>
<title>Discrimination between Similar Languages, Varieties and Dialects using CNN- and LSTM-based Deep Neural Networks</title>
</titleInfo>
<name type="personal">
<namePart type="given">Chinnappa</namePart>
<namePart type="family">Guggilla</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2016-dec</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial3)</title>
</titleInfo>
<originInfo>
<publisher>The COLING 2016 Organizing Committee</publisher>
<place>
<placeTerm type="text">Osaka, Japan</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>In this paper, we describe a system (CGLI) for discriminating similar languages, varieties and dialects using convolutional neural networks (CNNs) and long short-term memory (LSTM) neural networks. We have participated in the Arabic dialect identification sub-task of DSL 2016 shared task for distinguishing different Arabic language texts under closed submission track. Our proposed approach is language independent and works for discriminating any given set of languages, varieties, and dialects. We have obtained 43.29% weighted-F1 accuracy in this sub-task using CNN approach using default network parameters.</abstract>
<identifier type="citekey">guggilla-2016-discrimination</identifier>
<location>
<url>https://aclanthology.org/W16-4824</url>
</location>
<part>
<date>2016-dec</date>
<extent unit="page">
<start>185</start>
<end>194</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Discrimination between Similar Languages, Varieties and Dialects using CNN- and LSTM-based Deep Neural Networks
%A Guggilla, Chinnappa
%S Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial3)
%D 2016
%8 dec
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F guggilla-2016-discrimination
%X In this paper, we describe a system (CGLI) for discriminating similar languages, varieties and dialects using convolutional neural networks (CNNs) and long short-term memory (LSTM) neural networks. We have participated in the Arabic dialect identification sub-task of DSL 2016 shared task for distinguishing different Arabic language texts under closed submission track. Our proposed approach is language independent and works for discriminating any given set of languages, varieties, and dialects. We have obtained 43.29% weighted-F1 accuracy in this sub-task using CNN approach using default network parameters.
%U https://aclanthology.org/W16-4824
%P 185-194
Markdown (Informal)
[Discrimination between Similar Languages, Varieties and Dialects using CNN- and LSTM-based Deep Neural Networks](https://aclanthology.org/W16-4824) (Guggilla, 2016)
ACL