Skip to main content

Information Extraction from Hungarian, English and German CVs for a Career Portal

  • Conference paper
Mining Intelligence and Knowledge Exploration

Abstract

Recruiting employees is a serious issue for many enterprises. We propose here a procedure to automatically analyse uploaded CVs then prefill the application form which can save a considerable amount of time for applicants thus it increases user satisfaction. For this purpose, we shall introduce a high-recall CV parsing system for Hungarian, English and German. We comparatively evaluate two approaches for providing training data to our machine learning machinery and discuss other experiences gained.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. McCallum, A., Freitag, D., Pereira, F.: Maximum Entropy Markov Models for Information Extraction and Segmentation. In: Proceedings of the 17th International Conference on Machine Learning, pp. 591–598. Morgan Kaufmann Publishers Inc. (2000)

    Google Scholar 

  2. Sutton, C., McCallum, A.: An Introduction to Conditional Random Fields. ArXiv e-prints (2010)

    Google Scholar 

  3. Szarvas, G.: Feature Engineering for Domain Independent Named Entity Recognition and Biomedical Text Mining Applications. University of Szeged, Szeged (2008)

    Google Scholar 

  4. Dobó, A., Csirik, J.: Computing Semantic Similarity Using Large Static Corpora. In: van Emde Boas, P., Groen, F.C.A., Italiano, G.F., Nawrocki, J., Sack, H. (eds.) SOFSEM 2013. LNCS, vol. 7741, pp. 491–502. Springer, Heidelberg (2013)

    Google Scholar 

  5. Patil, S., Palshikar, G.K., Srivastava, R., Das, I.: Learning to Rank Resumes. In: Proceedings of FIRE 2012, ISI Kolkata, India (2012)

    Google Scholar 

  6. Yi, X., Allan, J., Croft, W.B.: Matching Resumes and Jobs Based on Relevance Models. In: Proceedings of SIGIR 2007, Amsterdam, The Netherlands, pp. 809–810 (2007)

    Google Scholar 

  7. Rode, H., Colen, R., Zavrel, J.: Semantic CV Search using Vacancies as Queries. In: Proceedings of the 12th Dutch-Belgian Information Retrieval Workshop, Ghent, Belgium, pp. 87–88 (2012)

    Google Scholar 

  8. Bollinger, J., Hardtke, D., Martin, B.: Using social data for resume job matching. In: Proceedings of DUBMMSM 2012, Maui, Hawaii, pp. 27–30 (2012)

    Google Scholar 

  9. Faliagka, E., Ramantas, K., Tsakalidis, A., Tzimas, G.: Application of Machine Learning Algorithms to an online Recruitment System. In: Proceedings of ICIW 2012, Stuttgart, Germany, pp. 215–220 (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Farkas, R. et al. (2014). Information Extraction from Hungarian, English and German CVs for a Career Portal. In: Prasath, R., O’Reilly, P., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. Lecture Notes in Computer Science(), vol 8891. Springer, Cham. https://doi.org/10.1007/978-3-319-13817-6_32

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-13817-6_32

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13816-9

  • Online ISBN: 978-3-319-13817-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics