Summary of Percore: a Deep Learning-based Framework For Persian Spelling Correction with Phonetic Analysis, by Seyed Mohammad Sadegh Dashti et al.
PERCORE: A Deep Learning-Based Framework for Persian Spelling Correction with Phonetic Analysis
by Seyed Mohammad Sadegh Dashti, Amid Khatibi Bardsiri, Mehdi Jafari Shahbazzadeh
First submitted to arxiv on: 20 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Persian spelling correction system integrates deep learning techniques with phonetic analysis to enhance accuracy and efficiency in natural language processing (NLP) for Persian. The approach combines a fine-tuned language representation model with deep contextual analysis and phonetic insights, effectively correcting both non-word and real-word spelling errors. This strategy is particularly effective in tackling the complexities of Persian spelling, including morphology and homophony. The system outperforms existing methods on a wide-ranging dataset, achieving F1-Scores of 0.890 for detecting real-word errors and 0.905 for correcting them. Additionally, it demonstrates strong capability in non-word error correction, achieving an F1-Score of 0.891. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new Persian spelling correction system that combines deep learning and phonetic analysis to improve accuracy and efficiency. This system is very good at correcting spelling mistakes in Persian language, which has many special features like complex words and similar sounding words. The system works well on a big dataset and performs better than other systems. |
Keywords
» Artificial intelligence » Deep learning » F1 score » Natural language processing » Nlp
