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        <title>WEKA in Forensic Authorship Analysis: A corpus-based approach of Saudi Authors</title>
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    <name type="personal">
        <namePart type="given">Mashael</namePart>
        <namePart type="family">AlAmr</namePart>
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    <name type="personal">
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            <title>Proceedings of the 17th International Conference on Natural Language Processing (ICON)</title>
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        <name type="personal">
            <namePart type="given">Pushpak</namePart>
            <namePart type="family">Bhattacharyya</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <namePart type="given">Dipti</namePart>
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            <namePart type="family">Sharma</namePart>
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        <name type="personal">
            <namePart type="given">Rajeev</namePart>
            <namePart type="family">Sangal</namePart>
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    <abstract>This is a pilot study that aims to explore the potential of using WEKA in forensic authorship analysis. It is a corpus-based research using data from Twitter collected from thirteen authors from Riyadh, Saudi Arabia. It examines the performance of unbalanced and balanced data sets using different classifiers and parameters of word grams. The attributes are dialect-specific linguistic features categorized as word grams. The findings further support previous studies in computational authorship identification.</abstract>
    <identifier type="citekey">alamr-atwell-2020-weka</identifier>
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        <url>https://aclanthology.org/2020.icon-main.34</url>
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%0 Conference Proceedings
%T WEKA in Forensic Authorship Analysis: A corpus-based approach of Saudi Authors
%A AlAmr, Mashael
%A Atwell, Eric
%Y Bhattacharyya, Pushpak
%Y Sharma, Dipti Misra
%Y Sangal, Rajeev
%S Proceedings of the 17th International Conference on Natural Language Processing (ICON)
%D 2020
%8 December
%I NLP Association of India (NLPAI)
%C Indian Institute of Technology Patna, Patna, India
%F alamr-atwell-2020-weka
%X This is a pilot study that aims to explore the potential of using WEKA in forensic authorship analysis. It is a corpus-based research using data from Twitter collected from thirteen authors from Riyadh, Saudi Arabia. It examines the performance of unbalanced and balanced data sets using different classifiers and parameters of word grams. The attributes are dialect-specific linguistic features categorized as word grams. The findings further support previous studies in computational authorship identification.
%U https://aclanthology.org/2020.icon-main.34
%P 257-260

Markdown (Informal)

[WEKA in Forensic Authorship Analysis: A corpus-based approach of Saudi Authors](https://aclanthology.org/2020.icon-main.34) (AlAmr & Atwell, ICON 2020)

• WEKA in Forensic Authorship Analysis: A corpus-based approach of Saudi Authors (AlAmr & Atwell, ICON 2020)

ACL

• Mashael AlAmr and Eric Atwell. 2020. WEKA in Forensic Authorship Analysis: A corpus-based approach of Saudi Authors. In Proceedings of the 17th International Conference on Natural Language Processing (ICON), pages 257–260, Indian Institute of Technology Patna, Patna, India. NLP Association of India (NLPAI).