Skip to main content

Knowledge-Poor Context-Sensitive Spelling Correction for Modern Greek

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNAI,volume 8445)

Abstract

In the present work a methodology for automatic spelling correction is proposed for common errors on Modern Greek homophones. The proposed methodology corrects the error by taking into account morphosyntactic information regarding the context of the orthographically ambiguous word. Our methodology is knowledge-poor because the information used is only the endings of the words in the context of the ambiguous word; as such it can be adapted even by simple editors for real-time spelling correction. We tested our method using Id3, C4.5, Nearest Neighbor, Naive Bayes and Random Forest as machine learning algorithms for correct spelling prediction. Experimental results show that the success rate of the above method is usually between 90% and 95% and sometimes approaching 97%. Synthetic Minority Oversampling was used to cope with the problem of class imbalance in our datasets.

Keywords

  • Context-sensitive spelling correction
  • Random Forest
  • Modern Greek
  • SMOTE
  • knowledge-poor spelling prediction
  • supervised learning
  • imbalanced dataset
  • minority class over-sampling

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-07064-3_29
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   99.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-07064-3
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   129.99
Price excludes VAT (USA)

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Kukich, K.: Techniques for automatically correcting words in text. ACM Computing Surveys 24(4), 377–439 (1992)

    CrossRef  Google Scholar 

  2. Damerau, F.J., Mays, E.: An Examination of Undetected Typing Errors. Information Processing and Management 25(6), 659–664 (1989)

    CrossRef  Google Scholar 

  3. Angell, R.C., Freund, G.E., Willett, P.: Automatic Spelling Correction Using A Trigram Similarity Measure. Information Processing and Management 19(4), 255–261 (1983)

    CrossRef  Google Scholar 

  4. Kashyap, R.L., Oommen, B.J.: Spelling correction using probabilistic methods. Pattern Recognition Letters 2, 147–154 (1984)

    CrossRef  Google Scholar 

  5. Mays, E., Damerau, F.J., Mercer, R.L.: Context Based Spelling Correction. Information Processing and Management 27(5), 517–522 (1991)

    CrossRef  Google Scholar 

  6. Golding, A.R., Roth, D.: A Winnow-Based Approach to Context-Sensitive Spelling Correction. Machine Learning 34, 107–130 (1999)

    CrossRef  MATH  Google Scholar 

  7. Carlson, A., Fette, I.: Memory-Based Context-Sensitive Spelling Correction at Web Scale. In: Proceedings of the IEEE International Conference on Machine Learning and Applications, ICMLA (2007)

    Google Scholar 

  8. Golding, A.R., Schabes, Y.: Combining Trigram-based and Feature-based Methods for Context-Sensitive Spelling Correction. In: Proceedings of the 34th Annual Meeting of the Association for Computational Linguistics, Santa Cruz, CA (1996)

    Google Scholar 

  9. Ingason, A.K., Jóhannsson, S.B., Rögnvaldsson, E., Loftsson, H., Helgadóttir, S.: Context-Sensitive Spelling Correction and Rich Morphology. In: Proceedings of the 17th Nordic Conference of Computational Linguistics NODALIDA, pp. 231–234. Northern European Association for Language Technology (NEALT), Tartu (2009)

    Google Scholar 

  10. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research 16(1), 321–357 (2002)

    MATH  Google Scholar 

  11. Schaback, J., Li, F.: Multi-Level Feature Extraction for Spelling Correction. In: IJCAI 2007 (2007)

    Google Scholar 

  12. Golding, A.R.: A Bayesian hybrid method for context-sensitive spelling correction. In: Proceedings of the Third Workshop on Very Large Corpora, Boston, MA, pp. 39–53 (1995)

    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

Sagiadinos, S., Gasteratos, P., Dragonas, V., Kalamara, A., Spyridonidou, A., Kermanidis, K. (2014). Knowledge-Poor Context-Sensitive Spelling Correction for Modern Greek. In: Likas, A., Blekas, K., Kalles, D. (eds) Artificial Intelligence: Methods and Applications. SETN 2014. Lecture Notes in Computer Science(), vol 8445. Springer, Cham. https://doi.org/10.1007/978-3-319-07064-3_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-07064-3_29

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07063-6

  • Online ISBN: 978-3-319-07064-3

  • eBook Packages: Computer ScienceComputer Science (R0)