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Knowledge-Poor Context-Sensitive Spelling Correction for Modern Greek

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


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.


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

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

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07063-6

  • Online ISBN: 978-3-319-07064-3

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