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    <titleInfo>
        <title>An Assessment of Language Identification Methods on Tweets and Wikipedia Articles</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Pedro</namePart>
        <namePart type="family">Vernetti</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Larissa</namePart>
        <namePart type="family">Freitas</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-jul</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the The Fourth Widening Natural Language Processing Workshop</title>
        </titleInfo>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Seattle, USA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Language identification is the task of determining the language which a given text is written. This task is important for Natural Language Processing and Information Retrieval activities. Two popular approaches for language identification are the N-grams and stopwords models. In this paper, these two models were tested on different types of documents such as short, irregular texts (tweets) and long, regular texts (Wikipedia articles).</abstract>
    <identifier type="citekey">vernetti-freitas-2020-assessment</identifier>
    <part>
        <date>2020-jul</date>
        <extent unit="page">
            <start>58</start>
            <end>60</end>
        </extent>
    </part>
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