Arabic Spelling Correction using Supervised Learning
Table 3: The incremental results after adding each error type model and applying them on the development set.
['Model name', 'Recall', 'Precision', 'F1 score']
First, we start with only the preprocessed undiacriticized word, then we added our edit error classifier. Adding the add before classifier was a great addition to the system as the system was able to increase the number of corrected errors significantly, notably the add before classifier proposed too many incorrect suggestions that decreased the precision. Then we added the merging correction technique. Finally we added the split error correction technique. The system corrects 9860 errors versus 16659 golden error corrections and proposed 17057 correction resulting in the final system recall of 0.5919, precision of 0.5781 and F1 score of 0.5849.