Short vowels and other diacritics are not part of written Arabic scripts. Exceptions are made for important political and religious texts and in scripts for beginning students of Arabic. Script without diacritics have considerable ambiguity because many words with different diacritic patterns appear identical in a diacritic-less setting. We propose in this paper a maximum entropy approach for restoring diacritics in a document. The approach can easily integrate and make effective use of diverse types of information; the model we propose integrates a wide array of lexical, segment-based and part-of-speech tag features. The combination of these feature types leads to a state-of-the-art diacritization model. Using a publicly available corpus (LDC's Arabic Treebank Part 3), we achieve a diacritic error rate of 5.1%, a segment error rate 8.5%, and a word error rate of 17.3%. In case-ending-less setting, we obtain a diacritic error rate of 2.2%, a segment error rate 4.0%, and a word error rate of 7.2%.
Zitouni et al. (Sun,) studied this question.