Analyzing and Providing Comprehensive Feedback for French CVS with Readerbench

Gabriel Gutu, Ionut Cristian Paraschiv, Mihai Dascălu, Gabriel Cristian, Stefan Trausan-Matu, Olivier Lepoivre

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    Abstract

    In their everyday activities, recruiters are faced with the difficult task of analyzing and judging the quality of a wide range of CVs. Both the content quality and the visual hues, such as colors and their overall structure, need to be considered. This article enhances previous researches with a larger dataset, refined indices, and a more advanced technique of parsing the input documents. After applying various processing techniques from ReaderBench, an advanced Natural Language Processing framework, on a manually annotated dataset of 96 positive and negative French CVs, several writing indices were determined and filtered by leveraging statistical analyses. In addition, our experiment introduces a web application in which users can submit, gather an evaluation, and acquire valuable feedback on their CV.
    Original languageEnglish
    Pages (from-to)17-28
    JournalPolytechnical University of Bucharest. Scientific Bulletin. Series C: Electrical Engineering and Computer Science
    Volume80
    Issue number2
    Publication statusPublished - 2018

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    Keywords

    • CV analysis
    • CV assessment
    • text cohesion
    • textual complexity
    • Natural Language Processing

    Cite this

    Gutu, Gabriel ; Paraschiv, Ionut Cristian ; Dascălu, Mihai ; Cristian, Gabriel ; Trausan-Matu, Stefan ; Lepoivre, Olivier. / Analyzing and Providing Comprehensive Feedback for French CVS with Readerbench. In: Polytechnical University of Bucharest. Scientific Bulletin. Series C: Electrical Engineering and Computer Science. 2018 ; Vol. 80, No. 2. pp. 17-28.
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    abstract = "In their everyday activities, recruiters are faced with the difficult task of analyzing and judging the quality of a wide range of CVs. Both the content quality and the visual hues, such as colors and their overall structure, need to be considered. This article enhances previous researches with a larger dataset, refined indices, and a more advanced technique of parsing the input documents. After applying various processing techniques from ReaderBench, an advanced Natural Language Processing framework, on a manually annotated dataset of 96 positive and negative French CVs, several writing indices were determined and filtered by leveraging statistical analyses. In addition, our experiment introduces a web application in which users can submit, gather an evaluation, and acquire valuable feedback on their CV.",
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    Analyzing and Providing Comprehensive Feedback for French CVS with Readerbench. / Gutu, Gabriel; Paraschiv, Ionut Cristian; Dascălu, Mihai; Cristian, Gabriel ; Trausan-Matu, Stefan; Lepoivre, Olivier.

    In: Polytechnical University of Bucharest. Scientific Bulletin. Series C: Electrical Engineering and Computer Science, Vol. 80, No. 2, 2018, p. 17-28.

    Research output: Contribution to journalArticleAcademicpeer-review

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    T1 - Analyzing and Providing Comprehensive Feedback for French CVS with Readerbench

    AU - Gutu, Gabriel

    AU - Paraschiv, Ionut Cristian

    AU - Dascălu, Mihai

    AU - Cristian, Gabriel

    AU - Trausan-Matu, Stefan

    AU - Lepoivre, Olivier

    PY - 2018

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    AB - In their everyday activities, recruiters are faced with the difficult task of analyzing and judging the quality of a wide range of CVs. Both the content quality and the visual hues, such as colors and their overall structure, need to be considered. This article enhances previous researches with a larger dataset, refined indices, and a more advanced technique of parsing the input documents. After applying various processing techniques from ReaderBench, an advanced Natural Language Processing framework, on a manually annotated dataset of 96 positive and negative French CVs, several writing indices were determined and filtered by leveraging statistical analyses. In addition, our experiment introduces a web application in which users can submit, gather an evaluation, and acquire valuable feedback on their CV.

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    KW - CV assessment

    KW - text cohesion

    KW - textual complexity

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