# Correcting Real-Word Spelling Errors: A New Hybrid Approach

arXiv: https://arxiv.org/abs/2302.06407 · HTML: https://mlsanity.com/paper/2302.06407/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 31 references that are in the map.
- First posted on arXiv 9 Feb 2023.

## Built on by (most cited first, 3 of 3)

- [PERCORE: A Deep Learning-Based Framework for Persian Spelling Correction with Phonetic Analysis](https://mlsanity.com/paper/2407.14789/) (2024)
- [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 one of the main tasks in the field of Natural Language Processing. Contrary to common spelling errors, real-word errors cannot be detected by conventional spelling correction methods. The real-word correction model proposed by Mays, Damerau and Mercer showed a great performance in different evaluations. In this research, however, a new hybrid approach is proposed which relies on statistical and syntactic knowledge to detect and correct real-word errors. In this model, Constraint Grammar (CG) is used to discriminate among sets of correction candidates in the search space. Mays, Damerau and Mercer's trigram approach is manipulated to estimate the probability of syntactically well-formed correction candidates. The approach proposed here is tested on the Wall Street Journal corpus. The model can prove to be more practical than some other models, such as WordNet-based method of Hirst and Budanitsky and fixed windows size method of Wilcox-O'Hearn and Hirst.