Analyzing and Providing Comprehensive Feedback for French CVS with Readerbench

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

Analyzing and Providing Comprehensive Feedback for French CVS with Readerbench

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

Research output: Contribution to journal › Article › Academic › peer-review

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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 language	English
Pages (from-to)	17-28
Journal	Polytechnical University of Bucharest. Scientific Bulletin. Series C: Electrical Engineering and Computer Science
Volume	80
Issue number	2
Publication status	Published - 2018
Externally published	Yes

Keywords

CV analysis
CV assessment
text cohesion
textual complexity
Natural Language Processing

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rez855_710711Final published version, 339 KBLicence: CC BY-NC-SA

Rage: Realising an Applied Gaming Eco-system
Westera, W., Georgiadis, K., Saveski, G., van Lankveld, G., Bahreini, K., van der Vegt, W., Berkhout, J., Nyamsuren, E., Kluijfhout, E. & Nadolski, R.
European Union
1/02/15 → 31/07/19
Project: Research

Cite this

@article{57fe48d60e484913aa072506448f66bc,

title = "Analyzing and Providing Comprehensive Feedback for French CVS with Readerbench",

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.",

keywords = "CV analysis, CV assessment, text cohesion, textual complexity, Natural Language Processing",

author = "Gabriel Gutu and Paraschiv, {Ionut Cristian} and Mihai Dasc{\u a}lu and Gabriel Cristian and Stefan Trausan-Matu and Olivier Lepoivre",

year = "2018",

language = "English",

volume = "80",

pages = "17--28",

journal = "Polytechnical University of Bucharest. Scientific Bulletin. Series C: Electrical Engineering and Computer Science",

issn = "2286-3540",

publisher = "Universitatea Politehnica din Bucuresti,Polytechnic University of Bucharest",

number = "2",

}

Analyzing and Providing Comprehensive Feedback for French CVS with Readerbench. / Gutu, Gabriel; Paraschiv, Ionut Cristian; Dascălu, Mihai et al.
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 journal › Article › Academic › peer-review

TY - JOUR

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

Y1 - 2018

N2 - 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.

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.

KW - CV analysis

KW - CV assessment

KW - text cohesion

KW - textual complexity

KW - Natural Language Processing

M3 - Article

SN - 2286-3540

VL - 80

SP - 17

EP - 28

JO - Polytechnical University of Bucharest. Scientific Bulletin. Series C: Electrical Engineering and Computer Science

JF - Polytechnical University of Bucharest. Scientific Bulletin. Series C: Electrical Engineering and Computer Science

IS - 2

ER -

Analyzing and Providing Comprehensive Feedback for French CVS with Readerbench

Abstract

Keywords

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Rage: Realising an Applied Gaming Eco-system

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