Summary of Araspell: a Deep Learning Approach For Arabic Spelling Correction, by Mahmoud Salhab and Faisal Abu-khzam
AraSpell: A Deep Learning Approach for Arabic Spelling Correction
by Mahmoud Salhab, Faisal Abu-Khzam
First submitted to arxiv on: 11 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 AraSpell framework is introduced, a seq2seq model architecture-based approach for Arabic spelling correction. The framework utilizes Recurrent Neural Network (RNN) and Transformer models with artificial data generation for error injection, trained on over 6.9 million Arabic sentences. Experimental studies demonstrate the effectiveness of AraSpell, achieving 4.8% word error rate (WER), 1.11% character error rate (CER), 2.9% CER, and 10.65% WER compared to labeled data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AraSpell is a new way to fix spelling mistakes in Arabic texts. It uses special types of artificial intelligence called seq2seq models to correct errors. These models are trained on huge amounts of Arabic text to learn how to make corrections. AraSpell does a great job correcting mistakes, with an error rate that’s much lower than other methods. |
Keywords
» Artificial intelligence » Cer » Neural network » Rnn » Seq2seq » Transformer
