AraSpell: A Deep Learning Approach for Arabic Spelling Correction

Mahmoud Salhab, Faisal Abu-Khzam
5/11/2024
cs.CLcs.AI

No official code found. Best implementation: msalhab96/AraSpell (25 stars, MIT, last commit 2024-07-21).

Abstract

Spelling correction is the task of identifying spelling mistakes, typos, and grammatical mistakes in a given text and correcting them according to their context and grammatical structure. This work introduces "AraSpell," a framework for Arabic spelling correction using different seq2seq model architectures such as Recurrent Neural Network (RNN) and Transformer with artificial data generation for error injection, trained on more than 6.9 Million Arabic sentences. Thorough experimental studies provide empirical evidence of the effectiveness of the proposed approach, which achieved 4.8% and 1.11% word error rate (WER) and character error rate (CER), respectively, in comparison with labeled data of 29.72% WER and 5.03% CER. Our approach achieved 2.9% CER and 10.65% WER in comparison with labeled data of 10.02% CER and 50.94% WER. Both of these results are obtained on a test set of 100K sentences.

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Code Implementations(1)

msalhab96/AraSpellCommunity100%
MIT

A framework for Arabic spelling correction using different seq2seq model architectures such as transformers and RNNs

252Python, DockerfileJul 29, 20222 years agoMIT
arabic-nlpautocorrectionnlpspelling-correction

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Cite this paper

@article{salhab2024araspell,
  title  = {AraSpell: A Deep Learning Approach for Arabic Spelling Correction},
  author = {Mahmoud Salhab and Faisal Abu-Khzam},
  year   = {2024},
  eprint = {2405.06981},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2405.06981v1}
}

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