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

  • Alessandro BondielliEmail author
  • Francesco Marcelloni
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Notes

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.

References

  1. 1.
    Bengio, Y., Ducharme, R., Vincent, P., Janvin, C.: A neural probabilistic language model. J. Mach. Learn. Res. 3, 1137–1155 (2003)zbMATHGoogle Scholar
  2. 2.
    Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching Word Vectors with Subword Information. arXiv e-prints arXiv:1607.04606
  3. 3.
    Church, K.W., Hanks, P.: Word association norms, mutual information, and lexicography. Comput. Linguist. 16(1), 22–29 (1990)Google Scholar
  4. 4.
    Dumais, S.T.: Enhancing Performance in Latent Semantic Indexing (LSI) Retrieval (1992)Google Scholar
  5. 5.
    Eckhardt, A., Laumer, S., Maier, C., Weitzel, T.: The transformation of people, processes, and IT in e-recruiting: Insights from an eight-year case study of a German media corporation. Empl. Relat. 36(4), 415–431 (2015)CrossRefGoogle Scholar
  6. 6.
    Evert, S.: Corpora and collocations. In: Lüdeling, S., Kytö, M. (eds.) Corpus Linguistics. An International Handbook, article 58, pp. 1212–1248 (2008)Google Scholar
  7. 7.
    Grave, E., Bojanowski, P., Gupta, P., Joulin, A., Mikolov, T.: Learning word vectors for 157 languages. In: Proceedings of the International Conference on Language Resources and Evaluation (LREC 2018) (2018)Google Scholar
  8. 8.
    Harris, Z.S.: Distributional structure. Word 10, 146–62 (1954)CrossRefGoogle Scholar
  9. 9.
    Heggo, I.A., Abdelbaki, N.: Hybrid information filtering engine for personalized job recommender system. In: Hassanien, A.E., Tolba, M.F., Elhoseny, M., Mostafa, M. (eds.) AMLTA 2018. AISC, vol. 723, pp. 553–563. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-74690-6_54CrossRefGoogle Scholar
  10. 10.
    Laumer, S., Maier, C., Eckhardt, A.: The impact of business process management and applicant tracking systems on recruiting process performance: an empirical study. J. Bus. Econ. 85, 421–453 (2014)Google Scholar
  11. 11.
    Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: Proceedings of the 31st International Conference on Machine Learning (ICML 2014), vol. 32 (2014)Google Scholar
  12. 12.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: ICLR Workshop (2013)Google Scholar
  13. 13.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of the 26th International Conference on Neural Information Processing Systems (NIPS 2013), vol. 2 (2013)Google Scholar
  14. 14.
    Peters, M.E., et al.: Deep contextualized word representations. arXiv e-prints arXiv:1802.05365
  15. 15.
    Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Shehu, V., Besimi, A.: Improving employee recruitment through data mining. In: Rocha, Á., Adeli, H., Reis, L.P., Costanzo, S. (eds.) WorldCIST 2018. AISC, vol. 745, pp. 194–202. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-77703-0_19CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.DINFOUniversity of FlorenceFlorenceItaly
  2. 2.Department of Information EngineeringUniversity of PisaPisaItaly

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