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
Number of pages	12
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
Natural Language Processing
text cohesion
textual complexity

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

Rage: Realising an Applied Gaming Eco-system
Westera, W. (PI), Georgiadis, K. (CoI), Saveski, G. (CoI), van Lankveld, G. (CoI), Bahreini, K. (CoI), van der Vegt, W. (CoI), Berkhout, J. (CoI), Nyamsuren, E. (CoI), Kluijfhout, E. (CoI) & Nadolski, R. (CoI)
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, Natural Language Processing, text cohesion, textual complexity",

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 - Natural Language Processing

KW - text cohesion

KW - textual complexity

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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