<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="attia-etal-2018-ghht">
    <titleInfo>
        <title>GHHT at CALCS 2018: Named Entity Recognition for Dialectal Arabic Using Neural Networks</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Mohammed</namePart>
        <namePart type="family">Attia</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Younes</namePart>
        <namePart type="family">Samih</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Wolfgang</namePart>
        <namePart type="family">Maier</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Gustavo</namePart>
            <namePart type="family">Aguilar</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Fahad</namePart>
            <namePart type="family">AlGhamdi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Victor</namePart>
            <namePart type="family">Soto</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Thamar</namePart>
            <namePart type="family">Solorio</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Mona</namePart>
            <namePart type="family">Diab</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Julia</namePart>
            <namePart type="family">Hirschberg</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Melbourne, Australia</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>This paper describes our system submission to the CALCS 2018 shared task on named entity recognition on code-switched data for the language variant pair of Modern Standard Arabic and Egyptian dialectal Arabic. We build a a Deep Neural Network that combines word and character-based representations in convolutional and recurrent networks with a CRF layer. The model is augmented with stacked layers of enriched information such pre-trained embeddings, Brown clusters and named entity gazetteers. Our system is ranked second among those participating in the shared task achieving an FB1 average of 70.09%.</abstract>
    <identifier type="citekey">attia-etal-2018-ghht</identifier>
    <identifier type="doi">10.18653/v1/W18-3212</identifier>
    <location>
        <url>https://aclanthology.org/W18-3212</url>
    </location>
    <part>
        <date>2018-07</date>
        <extent unit="page">
            <start>98</start>
            <end>102</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T GHHT at CALCS 2018: Named Entity Recognition for Dialectal Arabic Using Neural Networks
%A Attia, Mohammed
%A Samih, Younes
%A Maier, Wolfgang
%Y Aguilar, Gustavo
%Y AlGhamdi, Fahad
%Y Soto, Victor
%Y Solorio, Thamar
%Y Diab, Mona
%Y Hirschberg, Julia
%S Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F attia-etal-2018-ghht
%X This paper describes our system submission to the CALCS 2018 shared task on named entity recognition on code-switched data for the language variant pair of Modern Standard Arabic and Egyptian dialectal Arabic. We build a a Deep Neural Network that combines word and character-based representations in convolutional and recurrent networks with a CRF layer. The model is augmented with stacked layers of enriched information such pre-trained embeddings, Brown clusters and named entity gazetteers. Our system is ranked second among those participating in the shared task achieving an FB1 average of 70.09%.
%R 10.18653/v1/W18-3212
%U https://aclanthology.org/W18-3212
%U https://doi.org/10.18653/v1/W18-3212
%P 98-102

Markdown (Informal)

[GHHT at CALCS 2018: Named Entity Recognition for Dialectal Arabic Using Neural Networks](https://aclanthology.org/W18-3212) (Attia et al., ACL 2018)

• GHHT at CALCS 2018: Named Entity Recognition for Dialectal Arabic Using Neural Networks (Attia et al., ACL 2018)

ACL

• Mohammed Attia, Younes Samih, and Wolfgang Maier. 2018. GHHT at CALCS 2018: Named Entity Recognition for Dialectal Arabic Using Neural Networks. In Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching, pages 98–102, Melbourne, Australia. Association for Computational Linguistics.