Details

Autor(en) / Beteiligte

• Dashti, Seyed Mohammad Sadegh
• Khatibi Bardsiri, Amid
• Jafari Shahbazzadeh, Mehdi

Titel

PERCORE: A Deep Learning-Based Framework for Persian Spelling Correction with Phonetic Analysis

Ist Teil von

• International journal of computational intelligence systems, 2024-05, Vol.17 (1), p.1-23, Article 114

Ort / Verlag

Dordrecht: Springer Netherlands

Erscheinungsjahr

2024

Quelle

Elektronische Zeitschriftenbibliothek - Freely accessible e-journals

Beschreibungen/Notizen

• 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 capability in non-word error correction, achieving an F1-Score of 0.891. These results illustrate the significant benefits of incorporating phonetic insights into deep learning models for spelling correction. Our contributions not only advance Persian language processing by providing a versatile solution for a variety of NLP applications but also pave the way for future research in the field, emphasizing the critical role of phonetic analysis in developing effective spelling correction system.