In this work, we address the problem of spelling correction in the Arabic
language utilizing the new corpus provided by QALB (Qatar Arabic Language Bank)
project which is an annotated corpus of sentences with errors and their
corrections. The corpus contains edit, add before, split, merge, add after,
move and other error types. We are concerned with the first four error types as
they contribute more than 90% of the spelling errors in the corpus. The
proposed system has many models to address each error type on its own and then
integrating all the models to provide an efficient and robust system that
achieves an overall recall of 0.59, precision of 0.58 and F1 score of 0.58
including all the error types on the development set. Our system participated
in the QALB 2014 shared task "Automatic Arabic Error Correction" and achieved
an F1 score of 0.6, earning the sixth place out of nine participants.

利用 QALB 语料库中提供的经过标注的代表有错误和更正版本的句子，本文的系统使用不同的机器学习模型对四种类型的拼写错误（修改、前添加、拆分和合并）进行修正，最终在 QALB 2014 年 “自动阿拉伯语错误修正” 任务中获得了 6 名中的第六名，F1 得分为 0.6。