Said A., El-Sharqwi M., Chalabi A., Kamal E. (2013) A Hybrid Approach for Arabic Diacritization. In: Métais E., Meziane F., Saraee M., Sugumaran V., Vadera S. (eds) Natural Language Processing and Information Systems. NLDB 2013. Lecture Notes in Computer Science, vol 7934. Springer, Berlin, Heidelberg
The orthography of Modern standard Arabic (MSA) includes a set of special marks called diacritics that carry the intended pronunciation of words. Arabic text is usually written without diacritics which leads to major linguistic ambiguities in most of the cases since Arabic words have different meaning depending on how they are diactritized. This paper introduces a hybrid diacritization system combining both rule-based and data- driven techniques targeting standard Arabic text. Our system relies on automatic correction, morphological analysis, part of speech tagging and out of vocabulary diacritization components. The system shows improved results over the best reported systems in terms of full-form diacritization, and comparable results on the level of morphological diacritization. We report these results by evaluating our system using the same training and evaluation sets used by the systems we compare against.. Our system shows a word error rate (WER) of 4.4% on the morphological diacritization, ignoring the last letter diacritics, and 11.4% on the full-form diacritization including case ending diacritics. This means an absolute 1.1% reduction on the word error rate (WER) over the best reported system.
Arabic Arabic orthography diacritization vowelization morphology morphology features morphological analysis part-of-speech tagging automatic correction Viterbi case ending natural language processing language modeling conditional random fields CRF