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    <titleInfo>
        <title>Authorship Recognition with Short-Text using Graph-based Techniques</title>
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    <name type="personal">
        <namePart type="given">Laura</namePart>
        <namePart type="family">Cruz</namePart>
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        <dateIssued>2019-08</dateIssued>
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            <title>Proceedings of the 2019 Workshop on Widening NLP</title>
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        <name type="personal">
            <namePart type="given">Amittai</namePart>
            <namePart type="family">Axelrod</namePart>
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        <name type="personal">
            <namePart type="given">Diyi</namePart>
            <namePart type="family">Yang</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Rossana</namePart>
            <namePart type="family">Cunha</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Samira</namePart>
            <namePart type="family">Shaikh</namePart>
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        <name type="personal">
            <namePart type="given">Zeerak</namePart>
            <namePart type="family">Waseem</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>In recent years, studies of authorship recognition has aroused great interest in graph-based analysis. Modeling the writing style of each author using a network of co-occurrence words. However, short texts can generate some changes in the topology of network that cause impact on techniques of feature extraction based on graph topology. In this work, we evaluate the robustness of global-strategy and local-strategy based on complex network measurements comparing with graph2vec a graph embedding technique based on skip-gram model. The experiment consists of evaluating how each modification in the length of text affects the accuracy of authorship recognition on both techniques using cross-validation and machine learning techniques.</abstract>
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%0 Conference Proceedings
%T Authorship Recognition with Short-Text using Graph-based Techniques
%A Cruz, Laura
%Y Axelrod, Amittai
%Y Yang, Diyi
%Y Cunha, Rossana
%Y Shaikh, Samira
%Y Waseem, Zeerak
%S Proceedings of the 2019 Workshop on Widening NLP
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F cruz-2019-authorship
%X In recent years, studies of authorship recognition has aroused great interest in graph-based analysis. Modeling the writing style of each author using a network of co-occurrence words. However, short texts can generate some changes in the topology of network that cause impact on techniques of feature extraction based on graph topology. In this work, we evaluate the robustness of global-strategy and local-strategy based on complex network measurements comparing with graph2vec a graph embedding technique based on skip-gram model. The experiment consists of evaluating how each modification in the length of text affects the accuracy of authorship recognition on both techniques using cross-validation and machine learning techniques.
%U https://aclanthology.org/W19-3649
%P 153-156

Markdown (Informal)

[Authorship Recognition with Short-Text using Graph-based Techniques](https://aclanthology.org/W19-3649) (Cruz, WiNLP 2019)

• Authorship Recognition with Short-Text using Graph-based Techniques (Cruz, WiNLP 2019)

ACL

• Laura Cruz. 2019. Authorship Recognition with Short-Text using Graph-based Techniques. In Proceedings of the 2019 Workshop on Widening NLP, pages 153–156, Florence, Italy. Association for Computational Linguistics.