NDLI: SegCV : traitement efficace de CV avec analyse et correction d'erreurs

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SegCV : traitement efficace de CV avec analyse et correction d'erreurs

Content Provider	Semantic Scholar
Author	Cabrera-Diego, Luis Adrián Torres-Moreno, Juan-Manuel El-Bèze, Marc
Copyright Year	2013
Abstract	RESUME Le marche d'offres d'emploi et des candidatures sur Internet a connu, ces derniers temps, une croissance exponentielle. Ceci implique des volumes d'information (majoritairement sous la forme de textes libres) intraitables manuellement. Les CV sont dans des formats tres divers : .pdf, .doc, .dvi, .ps, etc., ce qui peut provoquer des erreurs lors de la conversion en texte plein. Nous proposons SegCV, un systeme qui a pour but l'analyse automatique des CV des candidats. Dans cet article, nous presentons des algorithmes reposant sur une analyse de surface, afin de segmenter les CV de maniere precise. Nous avons evalue la segmentation automatique selon des corpus de reference que nous avons constitues. Les experiences preliminaires realisees sur une grande collection de CV en francais avec correction du bruit montrent de bons resultats en precision, rappel et F-Score. ABSTRACT SegCV : Eficient parsing of resumes with analysis and correction of errors Over the last years, the online market of jobs and candidatures offers has reached an exponential growth. This has implied great amounts of information (mainly in a text free style) which cannot be processed manually. The resumes are in several formats : .pdf, .doc, .dvi, .ps, etc., that can provoque errors or noise during the conversion to plain text. We propose SegCV, a system that has as goal the automatic parsing of candidates' resumes. In this article we present the algoritms, which are based over a surface analysis, to segment the resumes in an accurate way. We evaluated the automatic segmentation using a reference corpus that we have created. The preliminary experiments, done over a large collection of resumes in French with noise correction, show good results in precision, recall and F-score. MOTS-CLES : RI, Ressources humaines, traitement de CV, Modele a base de regles.
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://lia.univ-avignon.fr/bibz/data/pdf/20131092605.pdf
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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