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Title: How Adequate is your CV? Analyzing French CVs with ReaderBench
Authors: Gutu, Gabriel
Dascalu, Mihai
Trausan-Matu, Stefan
Lepoivre, Olivier
Keywords: CV analysis
text cohesion
semantic relatedness
textual complexity
Natural Language Processing
Issue Date: 2017
Publisher: IEEE
Citation: Gutu, G., Dascalu, M., Trausan-Matu, S., & Lepoivre, O. (2017). How Adequate is your CV? Analyzing French CVs with ReaderBench. In 3rd Int. Workshop on Design and Spontaneity in Computer-Supported Collaborative Learning (DS-CSCL-2017), in conjunction with the 21th Int. Conf. on Control Systems and Computer Science (CSCS21) (pp. 559–565). Bucharest, Romania: IEEE.
Abstract: This study is aimed at presenting a new ReaderBench-based tool built to support candidates in increasing the quality of their CV for a job opening. Both the visual quality and the textual content are considered while also providing an overview and corresponding feedback for the entire CV. The presented CV analysis tool uses advanced Natural Language Processing techniques to interpret and understand the content from written texts, while also considering their visual traits. The study was performed on a collection of more than 50 CVs that were manually annotated as positive or negative in terms of their visual and content-oriented aspects. A statistical analysis based on more than 400 textual indices was performed on the training corpora in order to extract the traits that define a good commercial CV. The results enabled us to build an online tool accessible on our website that provides recommendations for CVs written in French language.
Appears in Collections:1. RAGE Publications

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