# Improving the quality of Persian clinical text with a novel spelling correction system

arXiv: https://arxiv.org/abs/2408.03622 · HTML: https://mlsanity.com/paper/2408.03622/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 123 references that are in the map.
- First posted on arXiv 7 Aug 2024.

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [ParsBERT: Transformer-based Model for Persian Language Understanding](https://mlsanity.com/paper/2005.12515/) (2020)
- [A Primer on Neural Network Models for Natural Language Processing](https://mlsanity.com/paper/1510.00726/) (2015)
- [NeuSpell: A Neural Spelling Correction Toolkit](https://mlsanity.com/paper/2010.11085/) (2020)
- [Towards Contextual Spelling Correction for Customization of End-to-end Speech Recognition Systems](https://mlsanity.com/paper/2203.00888/) (2022)
- [SINA-BERT: A pre-trained Language Model for Analysis of Medical Texts in Persian](https://mlsanity.com/paper/2104.07613/) (2021)
- [Misspelling Correction with Pre-trained Contextual Language Model](https://mlsanity.com/paper/2101.03204/) (2021)
- [Persian Handwritten Digit, Character and Word Recognition Using Deep Learning](https://mlsanity.com/paper/2010.12880/) (2020)
- [An Error-Guided Correction Model for Chinese Spelling Error Correction](https://mlsanity.com/paper/2301.06323/) (2023)
- [AraSpell: A Deep Learning Approach for Arabic Spelling Correction](https://mlsanity.com/paper/2405.06981/) (2024)
- [Unsupervised Context-Sensitive Spelling Correction of English and Dutch Clinical Free-Text with Word and Character N-Gram Embeddings](https://mlsanity.com/paper/1710.07045/) (2017)
- [Automatic Standardization of Colloquial Persian](https://mlsanity.com/paper/2012.05879/) (2020)
- [Real-Word Error Correction with Trigrams: Correcting Multiple Errors in a Sentence](https://mlsanity.com/paper/2302.04096/) (2023)
- [Detection is the central problem in real-word spelling correction](https://mlsanity.com/paper/1408.3153/) (2014)
- [Correcting Real-Word Spelling Errors: A New Hybrid Approach](https://mlsanity.com/paper/2302.06407/) (2023)

## Abstract (arXiv)

Background: The accuracy of spelling in Electronic Health Records (EHRs) is a critical factor for efficient clinical care, research, and ensuring patient safety. The Persian language, with its abundant vocabulary and complex characteristics, poses unique challenges for real-word error correction. This research aimed to develop an innovative approach for detecting and correcting spelling errors in Persian clinical text. Methods: Our strategy employs a state-of-the-art pre-trained model that has been meticulously fine-tuned specifically for the task of spelling correction in the Persian clinical domain. This model is complemented by an innovative orthographic similarity matching algorithm, PERTO, which uses visual similarity of characters for ranking correction candidates. Results: The evaluation of our approach demonstrated its robustness and precision in detecting and rectifying word errors in Persian clinical text. In terms of non-word error correction, our model achieved an F1-Score of 90.0% when the PERTO algorithm was employed. For real-word error detection, our model demonstrated its highest performance, achieving an F1-Score of 90.6%. Furthermore, the model reached its highest F1-Score of 91.5% for real-word error correction when the PERTO algorithm was employed. Conclusions: Despite certain limitations, our method represents a substantial advancement in the field of spelling error detection and correction for Persian clinical text. By effectively addressing the unique challenges posed by the Persian language, our approach paves the way for more accurate and efficient clinical documentation, contributing to improved patient care and safety. Future research could explore its use in other areas of the Persian medical domain, enhancing its impact and utility.