PERCORE: A Deep Learning-Based Framework for Persian Spelling Correction with Phonetic Analysis
Seyed Mohammad Sadegh Dashti, Amid Khatibi Bardsiri, Mehdi Jafari, Shahbazzadeh

TL;DR
This paper presents PERCORE, a deep learning framework that integrates phonetic analysis to improve Persian spelling correction, achieving high accuracy in correcting both non-word and real-word errors.
Contribution
The study introduces a novel Persian spelling correction system combining deep learning with phonetic analysis, addressing language-specific complexities and outperforming existing methods.
Findings
F1-Score of 0.890 for real-word error detection
F1-Score of 0.905 for real-word correction
F1-Score of 0.891 for non-word error correction
Abstract
This research introduces a state-of-the-art Persian spelling correction system that seamlessly integrates deep learning techniques with phonetic analysis, significantly enhancing the accuracy and efficiency of natural language processing (NLP) for Persian. Utilizing a fine-tuned language representation model, our methodology effectively combines deep contextual analysis with phonetic insights, adeptly correcting both non-word and real-word spelling errors. This strategy proves particularly effective in tackling the unique complexities of Persian spelling, including its elaborate morphology and the challenge of homophony. A thorough evaluation on a wide-ranging dataset confirms our system's superior performance compared to existing methods, with impressive F1-Scores of 0.890 for detecting real-word errors and 0.905 for correcting them. Additionally, the system demonstrates a strong…
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