Summary of A Comprehensive Approach to Misspelling Correction with Bert and Levenshtein Distance, by Amirreza Naziri et al.
A Comprehensive Approach to Misspelling Correction with BERT and Levenshtein Distance
by Amirreza Naziri, Hossein Zeinali
First submitted to arxiv on: 24 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 A novel neural network-based approach is proposed to identify and correct diverse spelling errors in text using BERT masked language models. The research leverages a comprehensive dataset of non-real-word and real-word errors categorized by type. Pre-trained BERT models are employed, and a combined approach utilizing the BERT model and Levenshtein distance is developed to optimize performance. Evaluation results demonstrate the system’s capabilities in correcting spelling mistakes, often outperforming existing systems tailored for the Persian language. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is found to fix writing errors using special computer models. These models are trained on a big dataset of correct and incorrect words. The goal is to make the best possible writer by giving it the ability to recognize and correct spelling mistakes. The results show that this approach works well, especially for languages like Persian. |
Keywords
» Artificial intelligence » Bert » Neural network
