# Real-Word Error Correction with Trigrams: Correcting Multiple Errors in a Sentence

arXiv: https://arxiv.org/abs/2302.04096 · HTML: https://mlsanity.com/paper/2302.04096/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 18 references that are in the map.
- First posted on arXiv 7 Feb 2023.

## Built on by (most cited first, 4 of 4)

- [PERCORE: A Deep Learning-Based Framework for Persian Spelling Correction with Phonetic Analysis](https://mlsanity.com/paper/2407.14789/) (2024)
- [Persian Typographical Error Type Detection Using Deep Neural Networks on Algorithmically-Generated Misspellings](https://mlsanity.com/paper/2305.11731/) (2023)
- [Automatic Real-word Error Correction in Persian Text](https://mlsanity.com/paper/2407.14795/) (2024)
- [Improving the quality of Persian clinical text with a novel spelling correction system](https://mlsanity.com/paper/2408.03622/) (2024)

## Abstract (arXiv)

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 (PCFG) 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.-O'Hearn, Hirst, and Budanitsky's fixed windows size method.