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 , segmentbased 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 % . Modern Arabic written texts are composed of scripts without short vowels and other diacritic marks . This often leads to considerable ambiguity since several words that have different diacritic patterns may appear identical in a diacritic-less setting . Educated modern Arabic speakers are able to accurately restore diacritics in a document . This is based on the context and their knowledge of the grammar and the lexicon of Arabic . However , a text without diacritics becomes a source of confusion for beginning readers and people with learning disabilities . A text without diacritics is also problematic for applications such as text-to-speech or speech-to-text , where the lack of diacritics adds another layer of ambiguity when processing the data . As an example , full vocalization of text is required for text-to-speech applications , where the mapping from graphemes to phonemes is simple compared to languages such as English and French ; where there is , in most cases , one-to-one relationship . Also , using data with diacritics shows an improvement in the accuracy of speech-recognition applications ( Afify et al . , 2004 ) . Currently , text-tospeech , speech-to-text , and other applications use data where diacritics are placed manually , which is a tedious and time consuming excercise . A diacritization system that restores the diacritics of scripts , i.e. supply the full diacritical markings , would be of interest to these applications . It also would greatly benefit nonnative speakers , sufferers of dyslexia and could assist in restoring diacritics of children 's and poetry books , a task that is currently done manually . We propose in this paper a statistical approach that restores diacritics in a text document . The proposed approach is based on the maximum entropy framework where several diverse sources of information are employed . The model implicitly learns the correlation between these types of information and the output diacritics . In the next section , we present the set of diacritics to be restored and the ambiguity we face when processing a non-diacritized text . Section 3 gives a brief summary of previous related works . Section 4 presents our diacritization model ; we explain the training and decoding process as well as the different feature categories employed to restore the diacritics . Section 5 describes a clearly defined and replicable split of the LDC 's Arabic Treebank Part 3 corpus , used to built and evaluate the system , so that the reproduction of the results and future comparison can accurately be established . Section 6 presents the experimental results . Section 7 reports a comparison of our approach to the finite state machine modeling technique that showed promissing results in ( Nelken and Shieber , 2005 ) . Finally , section 8 concludes the paper and discusses future directions . We presented in this paper a statistical model for Arabic diacritic restoration . The approach we propose is based on the Maximum entropy framework , which gives the system the ability to integrate different sources of knowledge . Our model has the advantage of successfully combining diverse sources of information ranging from lexical , segment-based and POS features . Both POS and segment-based features are generated by separate statistical systems -not extracted manually -in order to simulate real world applications . The segment-based features are extracted from a statistical morphological analysis system using WFST approach and the POS features are generated by a parsing model Table 5 : Error Rate in % for n-gram diacritic restoration and segmentation using FST and Kneser-Ney LM . Columns marked with " True shadda " represent results on documents containing the original consonant doubling " shadda " while columns marked with " Predicted shadda " represent results where the system restored all diacritics including shadda . that also uses Maximum entropy framework . Evaluation results show that combining these sources of information lead to state-of-the-art performance . As future work , we plan to incorporate Buckwalter morphological analyzer information to extract new features that reduce the search space . One idea will be to reduce the search to the number of hypotheses , if any , proposed by the morphological analyzer . We also plan to investigate additional conjunction features to improve the accuracy of the model .

Challenge: Short vowels and other diacritics are not expressed in written Arabic, making it difficult to read for beginner readers or system developments.
Approach: They propose an approach that uses maximum entropy to restore diacritics in Arabic documents by learning relations between a wide varieties of features.
Outcome: They show that by taking various kinds of features their system outperforms the existing state-of-the-art decritization model.