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Knowledge-Poor Context-Sensitive Spelling Correction for Modern Greek

机译:现代希腊语的知识匮乏的上下文敏感拼写校正

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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.
机译:在当前的工作中,提出了一种自动拼写校正的方法,用于解决现代希腊同音字上的常见错误。所提出的方法通过考虑关于正字法模棱两可的单词的上下文的词法信息来纠正错误。我们的方法学知识匮乏,因为在模棱两可的单词的上下文中,所使用的信息只是单词的结尾;因此,即使是简单的编辑器也可以对其进行修改,以进行实时拼写校正。我们使用Id3,C4.5,最近邻居,朴素贝叶斯和随机森林作为机器学习算法进行了测试,以进行正确的拼写预测。实验结果表明,上述方法的成功率通常在90%到95%之间,有时接近97%。综合少数群体过采样用于解决我们数据集中的类不平衡问题。

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