Skip to main content

Statistical Stemmers: A Reproducibility Study

Part of the Lecture Notes in Computer Science book series (LNISA,volume 10772)


Statistical stemmers are important components of Information Retrieval (IR) systems, especially for text search over languages with few linguistic resources. In recent years, research on stemmers produced relevant results, especially in 2011 when three language-independent stemmers were published in relevant venues. In this paper, we describe our efforts for reproducing these three stemmers. We also share the code as open-source and an extended version of Terrier system integrating the developed stemmers.

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD   89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   119.99
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

Learn about institutional subscriptions


  1. 1.

  2. 2.

  3. 3.

  4. 4.

  5. 5.

  6. 6.


  1. Di Nunzio, G.M., Ferro, N., Mandl, T., Peters, C.: CLEF 2007: ad hoc track overview. In: Peters, C., et al. (eds.) CLEF 2007. LNCS, vol. 5152, pp. 13–32. Springer, Heidelberg (2008).

    CrossRef  Google Scholar 

  2. Dietz, F., Petras, V.: A component-level analysis of an academic search test collection. In: Jones, G.J.F., et al. (eds.) CLEF 2017. LNCS, vol. 10456, pp. 29–42. Springer, Cham (2017).

    CrossRef  Google Scholar 

  3. Dolamic, L., Savoy, J.: Indexing and stemming approaches for the Czech language author links open overlay panel. Inf. Proces. Manage. 45(6), 714–720 (2009)

    CrossRef  Google Scholar 

  4. Ferro, N., Silvello, G.: CLEF 15th birthday: what can we learn from ad hoc retrieval? In: Kanoulas, E., Lupu, M., Clough, P., Sanderson, M., Hall, M., Hanbury, A., Toms, E. (eds.) CLEF 2014. LNCS, vol. 8685, pp. 31–43. Springer, Cham (2014).

    Google Scholar 

  5. Krovetz, R.: Viewing morphology as an inference process. In: Proceedings of 16th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 1993), pp. 191–202. ACM Press (1993)

    Google Scholar 

  6. Lovins, J.B.: Development of a Stemming algorithm. Mech. Transl. Comput. Linguist. 11(1/2), 22–31 (1968)

    Google Scholar 

  7. Macdonald, C., McCreadie, R., Santos, R.L.T., Ounis, I.: From puppy to maturity: experiences in developing terrier. In: Proceedings of OSIR at SIGIR, pp. 60–63 (2012)

    Google Scholar 

  8. Paik, J.H., Mitra, M., Parui, S.K., Järvelin, K.: GRAS: an effective and efficient stemming algorithm for information retrieval. ACM Trans. Inf. Syst. 29(4), 19 (2011)

    CrossRef  Google Scholar 

  9. Paik, J.H., Pal, D., Parui, S.K.: A novel corpus-based stemming algorithm using co-occurrence statistics. In: Proceedings of 34th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2011), pp. 863–872. ACM Press (2011)

    Google Scholar 

  10. Paik, J.H., Parui, S.K.: A fast corpus-based stemmer. ACM Trans. Asian Lang. Inf. Process. 10(2), 1–16 (2011)

    CrossRef  Google Scholar 

  11. Porter, M.F.: An algorithm for suffix stripping. Program 14(3), 130–137 (1980)

    CrossRef  Google Scholar 

  12. Savoy, J.: Searching strategies for the Hungarian language. Inf. Process. Manage. 44(1), 310–324 (2008)

    CrossRef  Google Scholar 

  13. Singh, J., Gupta, V.: Text stemming: approaches, applications, and challenges. ACM Comput. Surv. (CSUR) 49(3), 45:1–45:46 (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Gianmaria Silvello .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Silvello, G. et al. (2018). Statistical Stemmers: A Reproducibility Study. In: Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds) Advances in Information Retrieval. ECIR 2018. Lecture Notes in Computer Science(), vol 10772. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-76940-0

  • Online ISBN: 978-3-319-76941-7

  • eBook Packages: Computer ScienceComputer Science (R0)