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Real-word error correction with trigrams: correcting multiple errors in a sentence

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Abstract

Spelling correction is a fundamental task in text mining. In this study, we assess the real-word error correction model proposed by Mays, Damerau and Mercer and describe several drawbacks of the model. We propose a new variation which focuses on detecting and correcting multiple real-word errors in a sentence, by manipulating a probabilistic context-free grammar to discriminate between items in the search space. We test our approach on the Wall Street Journal corpus and show that it outperforms Hirst and Budanitsky’s WordNet-based method and Wilcox-O’Hearn, Hirst, and Budanitsky’s fixed windows size method.

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Authors and Affiliations

  1. Department of Computer Engineering, Kerman Branch, Islamic Azad University, Kerman, Iran

    Seyed MohammadSadegh Dashti

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  1. Seyed MohammadSadegh Dashti
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Dashti, S.M. Real-word error correction with trigrams: correcting multiple errors in a sentence. Lang Resources & Evaluation 52, 485–502 (2018). https://doi.org/10.1007/s10579-017-9397-4

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  • DOI: https://doi.org/10.1007/s10579-017-9397-4

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