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A Data-Driven Approach to Automatic Extraction of Professional Figure Profiles from Résumés

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Intelligent Data Engineering and Automated Learning – IDEAL 2019 (IDEAL 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11871))

Abstract

The process of selecting and interviewing suitable candidates for a job position is time-consuming and labour-intensive. Despite the existence of software applications aimed at helping professional recruiters in the process, only recently with Industry 4.0 there has been a real interest in implementing autonomous and data-driven approaches that can provide insights and practical assistance to recruiters.

In this paper, we propose a framework that is aimed at improving the performances of an Applicant Tracking System. More specifically, we exploit advanced Natural Language Processing and Text Mining techniques to automatically profile resources (i.e. candidates for a job) and offers by extracting relevant keywords and building a semantic representation of résumés and job opportunities.

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Notes

  1. 1.

    https://www.linkedin.com.

  2. 2.

    http://www.itpartneritalia.com/.

  3. 3.

    https://spacy.io/.

  4. 4.

    https://radimrehurek.com/gensim/models/doc2vec.html.

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Acknowledgements

This work was partially supported by Tuscany Region in the context of the project TALENT, “FESR 2014-2020". We wish to thank Dr. Alessio Ciardini, CEO of IT Partner Italia, and Filippo Giunti, IT manager of IT Partner Italia, for providing us with the data and for the invaluable support and insight, both theoretical and practical, on the recruiting field from a company perspective.

Author information

Authors and Affiliations

  1. DINFO, University of Florence, Florence, Italy

    Alessandro Bondielli

  2. Department of Information Engineering, University of Pisa, Pisa, Italy

    Alessandro Bondielli & Francesco Marcelloni

Authors
  1. Alessandro Bondielli
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  2. Francesco Marcelloni
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Corresponding author

Correspondence to Alessandro Bondielli .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, UK

    Hujun Yin

  2. Technical University of Madrid, Madrid, Spain

    David Camacho

  3. University of Birmingham, Birmingham, UK

    Peter Tino

  4. University of Huelva, Huelva, Spain

    Antonio J. Tallón-Ballesteros

  5. University of Exeter, Exeter, UK

    Ronaldo Menezes

  6. University of Manchester, Manchester, UK

    Richard Allmendinger

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Bondielli, A., Marcelloni, F. (2019). A Data-Driven Approach to Automatic Extraction of Professional Figure Profiles from Résumés. In: Yin, H., Camacho, D., Tino, P., Tallón-Ballesteros, A., Menezes, R., Allmendinger, R. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2019. IDEAL 2019. Lecture Notes in Computer Science(), vol 11871. Springer, Cham. https://doi.org/10.1007/978-3-030-33607-3_18

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  • DOI: https://doi.org/10.1007/978-3-030-33607-3_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33606-6

  • Online ISBN: 978-3-030-33607-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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