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.
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References
Kukich, K.: Techniques for automatically correcting words in text. ACM Computing Surveys 24(4), 377–439 (1992)
Damerau, F.J., Mays, E.: An Examination of Undetected Typing Errors. Information Processing and Management 25(6), 659–664 (1989)
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)
Kashyap, R.L., Oommen, B.J.: Spelling correction using probabilistic methods. Pattern Recognition Letters 2, 147–154 (1984)
Mays, E., Damerau, F.J., Mercer, R.L.: Context Based Spelling Correction. Information Processing and Management 27(5), 517–522 (1991)
Golding, A.R., Roth, D.: A Winnow-Based Approach to Context-Sensitive Spelling Correction. Machine Learning 34, 107–130 (1999)
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)
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)
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)
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)
Schaback, J., Li, F.: Multi-Level Feature Extraction for Spelling Correction. In: IJCAI 2007 (2007)
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)
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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. https://doi.org/10.1007/978-3-319-07064-3_29
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DOI: https://doi.org/10.1007/978-3-319-07064-3_29
Publisher Name: Springer, Cham
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