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Rashwan M.A.A.,Engineering Company for the Development of Computer Systems RDI | Rashwan M.A.A.,Cairo University | Al Sallab A.A.,Valeo Interbranch Automotive Software | Al Sallab A.A.,Cairo University | And 2 more authors.
IEEE Transactions on Audio, Speech and Language Processing | Year: 2015

The Arabic language belongs to a group of languages that require diacritization over their characters. Modern Standard Arabic (MSA) transcripts omit the diacritics, which are essential for many machine learning tasks like Text-To-Speech (TTS) systems. In this work Arabic diacritics restoration is tackled under a deep learning framework that includes the Confused Sub-set Resolution (CSR) method to improve the classification accuracy, in addition to an Arabic Part-of-Speech (PoS) tagging framework using deep neural nets. Special focus is given to syntactic diacritization, which still suffers low accuracy as indicated in prior works. Evaluation is done versus state-of-the-art systems reported in literature, with quite challenging datasets collected from different domains. Standard datasets like the LDC Arabic Tree Bank are used in addition to custom ones we have made available online to allow other researchers to replicate these results. Results show significant improvement of the proposed techniques over other approaches, reducing the syntactic classification error to 9.9% and morphological classification error to 3% compared to 12.7% and 3.8% of the best reported results in literature, improving the error by 22% over the best reported systems. © 2014 IEEE. Source


Rashwan M.A.A.,Engineering Company for the Development of Computer Systems RDI | Rashwan M.A.A.,Cairo University | Al-Badrashiny M.A.S.A.A.,Engineering Company for the Development of Computer Systems RDI | Attia M.,Engineering Company for the Development of Computer Systems RDI | And 3 more authors.
IEEE Transactions on Audio, Speech and Language Processing | Year: 2011

This paper introduces a large-scale dual-mode stochastic system to automatically diacritize raw Arabic text. The first of these modes determines the most likely diacritics by choosing the sequence of full-form Arabic word diacritizations with maximum marginal probability via A* lattice search and long-horizon n-grams probability estimation. When full-form words are OOV, the system switches to the second mode which factorizes each Arabic word into all its possible morphological constituents, then uses also the same techniques used by the first mode to get the most likely sequence of morphemes, hence the most likely diacritization. While the second mode achieves a far better coverage of the highly derivative and inflective Arabic language, the first mode is faster to learn, i.e., yields better disambiguation results for the same size of training corpora, especially for inferring syntactical (case-ending) diacritics. Our presented hybrid system that benefits from the advantages of both modes has experimentally been found superior to the best performing reported systems of Habash and Rambow, and of Zitouni , using the same training and test corpus for the sake of fair comparison. The word error rates of (morphological diacritization, overall diacritization including the case endings) for the three systems are, respectively, as follows (3.1%, 12.5%), (5.5%, 14.9%), and (7.9%, 18%). The hybrid architecture of language factorizing and unfactorizing components may be inspiring to other NLP/HLT problems in analogous situations. © 2010 IEEE. Source

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