Skip to main content

The Effect of Corpora Size on Performance of Named Entity Recognition

  • Chapter
  • First Online:
  • 1521 Accesses

Part of the book series: Studies in Big Data ((SBD,volume 27))

Abstract

The amount of on-line text available is continuously growing and has reached hundreds of billions of words. A lot of research has been done using this data, trying to improve results on different problems. Algorithms are continuously optimized, tested and compared after training on corpora with only one million words or less. Most research focuses on the accuracy of the results generated by these algorithms often overlooking the running time or the cost associated with running those algorithms. The main goal of this paper is to show the effect that large data has on the running time and performance of those algorithms in Natural Language Processing. To achieve this goal, three Named Entity Recognition tools were selected. We evaluated the trade-off between quality, running time, and the effect of increasing the data size on performance on the best variety of tools in NER domain. The result shows that the existing tools are unable to work with increasing data size. Also by increasing data size quality is increasing but performance is decreasing; therefore, rendering the existing tools inefficient. By optimizing these tools, large data sizes can be processed; unfortunately, latency is still high.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   99.99
Price excludes VAT (USA)
  • Durable hardcover 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

References

  1. Baeza-Yates, R. Big data or right data? In Mendelzon A, editor. Workshop, vol. 2013. 2013.

    Google Scholar 

  2. Gudivada V, Baeza-Yates R, Raghavan V. Big data: promises and problems. IEEE Comput Soc. 2015;48(03):20–3.

    Article  Google Scholar 

  3. Ekbal A, Sourjikova E, Frank A, Ponzetto S. Assessing the challenge of fine-grained named entity recognition and classification. In: NEWS’10 proceedings of the 2010 named entities workshop, 2010. p. 93–101.

    Google Scholar 

  4. Zhang L, Pan Y, Zhang T. Focused named entity recognition using machine learning. In: The 27th annual international ACM SIGIR conference on Research and development in information retrieval, 2004. p. 281–288.

    Google Scholar 

  5. Nadeau D, Sekine S. A survey of named entity recognition and classification. Int J Linguist Lang Resour. 2007;30(1):3–26.

    Google Scholar 

  6. Florian R, Ittycheriah A, Jing H, Zhang T. Named entity recognition through classifier combination. In: Proceeding CONLL ‘03 proceedings of the seventh conference on natural language learning at HLT-NAACL, vol. 4, 2003. p. 168–171.

    Google Scholar 

  7. Mansouri A, Suriani Affendey L, Mamat A. Named entity recognition approaches. Int J Comput Sci Net Secur. 2008;8:339–44.

    Google Scholar 

  8. Zhou GD, Su J. Named entity recognition using an HMM-based chunk tagger. In: 40th annual meeting on ACL, 2001. p. 473–80.

    Google Scholar 

  9. Alias-i. LingPipe 4.1.0 (2008, 22 Feb 2013). Available: http://alias-i.com/lingpipe

  10. Finkel JR, Grenager T, Manning C. Incorporating non-local information into information extraction systems by Gibbs sampling. In: 43rd annual meeting on ACL, 2005. p. 363–370.

    Google Scholar 

  11. Labatut V. Improved named entity recognition through SVM-based combination. 2013. <hal-01322867>

    Google Scholar 

  12. Ratinov L, Roth D. Design challenges and misconceptions in named entity recognition. In: 13th Conference on computational natural language learning, 2009. p. 147–155.

    Google Scholar 

  13. Mansouri A, Affendey LS, Mamat A. Named entity recognition approaches. International Journal of Computer Science and Network Security. 2008;8(2)

    Google Scholar 

  14. Erik, TKS, Fien, DM. 2003. Available http://www.cnts.ua.ac.be/conll2003/ner/000README

  15. Lewis DD, Yang Y, Rose TG, Li F. Rcv1: a new benchmark collection for text categorization research. J Mach Learn Res. 2004;5:361–97.

    Google Scholar 

  16. Lewis, D. D., Yang, Y., Rose, T. G., Li, F. 2015. http://trec.nist.gov/data/reuters/reuters.html

  17. Han J, Kamber M, Pei J. Data mining: concepts and techniques. 3rd ed. San Francisco: Morgan Kaufmann Publishers Inc.; 2011.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zeinab Liaghat .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Cite this chapter

Liaghat, Z. (2018). The Effect of Corpora Size on Performance of Named Entity Recognition. In: Moshirpour, M., Far, B., Alhajj, R. (eds) Highlighting the Importance of Big Data Management and Analysis for Various Applications. Studies in Big Data, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-319-60255-4_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-60255-4_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60254-7

  • Online ISBN: 978-3-319-60255-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics