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.
|Journal||Polytechnical University of Bucharest. Scientific Bulletin. Series C: Electrical Engineering and Computer Science|
|Publication status||Published - 2018|
- CV analysis
- CV assessment
- text cohesion
- textual complexity
- Natural Language Processing
Gutu, G., Paraschiv, I. C., Dascălu, M., Cristian, G., Trausan-Matu, S., & Lepoivre, O. (2018). Analyzing and Providing Comprehensive Feedback for French CVS with Readerbench. Polytechnical University of Bucharest. Scientific Bulletin. Series C: Electrical Engineering and Computer Science, 80(2), 17-28.