Ali Mijlad
ABSTRACT
As recurrent neural networks and statistic models continue to give better results in different fields in science and because Arabic text diacritization is paramount important for many Arabic language processing tasks, we are going in this paper to present an overview of systems handling that problem. Besides, we are going to propose an automatic diacritic restoration system for Arabic texts. We propose here an approach using Long Short Term Memory LSTM network and Alkhalil Morpho Sys2.
- Abandah, G.A., Graves, A., Al-Shagoor, B., Arabiyat, A., Jamour, F. and Al-Taee, M. 2015. Automatic diacritization of Arabic text using recurrent neural networks. International Journal on Document Analysis and Recognition (IJDAR). 18, 2 (2015), 183--197. Google Scholar
Digital Library
- Abed, S., Alshayeji, M. and Sultan, S. 2019. Diacritics Effect on Arabic Speech Recognition. Arabian Journal for Science and Engineering. (2019). Google ScholarCross Ref
- Alansary, S. 2017. Alserag: An Automatic Diacritization System for Arabic. Shaalan K., Hassanien A., Tolba F. (eds) Intelligent Natural Language Processing: Trends and Applications. Studies in Computational Intelligence. 523--543.Google Scholar
- Alnefaie, R. and Azmi, A.M. 2017. Automatic minimal diacritization of Arabic texts. Proceedings of the 3rd International Conference on Arabic Computational Linguistics, ACLing 2017, 5--6 November, Dubai, United Arab Emirates (2017), 169--174.Google Scholar
- Alqudah, S., Abandah, G. and Arabiyat, A. 2017. Investigating Hybrid Approaches for Arabic Text Diacritization with Recurrent Neural Networks. 2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT) (2017).Google Scholar
- Azmi, A.M. and Almajed, R.S. 2013. A survey of automatic Arabic diacritization techniques. Natural Language Engineering. 21, 3 (2013), 477--495. Google ScholarCross Ref
- Bebah, M., Amine, C., Azzeddine, M. and Abdelhak, L. 2014. Hybrid approaches for automatic vowelization of arabic texts. International Journal on Natural Language Computing (IJNLC). 3, 4 (2014), 53--71.Google ScholarCross Ref
- Belinkov, Y. and Glass, J. 2015. Arabic Diacritization with Recurrent Neural Networks. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal, 17--21 September (2015), 2281--2285.Google ScholarCross Ref
- Boudchiche, M., Mazroui, A., Ould, M. and Ould, A. 2017. AlKhalil Morpho Sys 2: A robust Arabic morphosyntactic analyzer. Journal of King Saud University - Computer and Information Sciences. 29, 2 (2017), 141--146. Google ScholarDigital Library
- Chennoufi, A. and Mazroui, A. 2016. Morphological, syntactic and diacritics rules for automatic diacritization of Arabic sentences. Journal of King Saud University - Computer and Information Sciences. 29, 2 (2016), 156--163. Google ScholarDigital Library
- Darwish, K., Mubarak, H. and Abdelali, A. 2017. Arabic Diacritization: Stats, Rules, and Hacks. Proceedings of The Third Arabic Natural Language Processing Workshop (WANLP), Valencia, Spain, April 3 (2017), 9--17.Google ScholarCross Ref
- Diab, M., Ghoneim, M. and Habash, N. 2007. Arabic Diacritization in the Context of Statistical Machine Translation. Proceedings The 11th Machine Translation Summit (MT-Summit XI), At Copenhagen, Denmark (2007).Google Scholar
- Elshafei, M., Al-Muhtaseb, H. and Alghamdi, M.M. 2006. Statistical methods for automatic diacritization of Arabic text. Proceedings 18th National computer Conference, Riyadh, March 26--29 (2006), 301--306.Google Scholar
- Fadel, A., Tuffaha, I., Al-Jawarneh, B. and Al-Ayyoub, M. 2019. Arabic Text Diacritization Using Deep Neural Networks. International conference on Computer Applications & Information Security, At Riyadh, Saudi Arabia (2019), 2--9.Google Scholar
- Fashwan, A. and Alansary, S. 2016. A Rule Based Method for Adding Case Ending Diacritics for Modern Standard Arabic Texts. proceedings of the 16th Conference on Language Engineering, At Cairo, Egypt (2016).Google Scholar
- Fashwan, A. and Alansary, S. 2017. SHAKKIL: an automatic diacritization system for modern standard Arabic texts. Proceedings of The Third Arabic Natural Language Processing Workshop (WANLP), Valencia, Spain, April 3 (2017), 84--93.Google ScholarCross Ref
- Gal, Y. 2002. An HMM approach to vowel restoration in Arabic and Hebrew. Proceedings of the ACL-02 workshop on Computational approaches to semitic languages, (2002), 1--7.Google ScholarDigital Library
- Hamed, O. and Zesch, T. 2017. A Survey and Comparative Study of Arabic Diacritization Tools. In Journal for Language Technology and Computational Linguistics JLCL. 32, 1 (2017), 27--47.Google ScholarCross Ref
- Metwally, A.S. and Rashwan, M.A. 2016. A Multi-Layered Approach for Arabic Text Diacritization. IEEE International Conference on Cloud Computing and Big Data Analysis (2016), 389--393.Google Scholar
- Mohamed Attia Mohamed Elaraby Ahmed 2000. A LARGE-SCALE COMPUTATIONAL PROCESSOR OF THE ARABIC MORPHOLOGY, AND APPLICATIONS. Thesis submitted to the of Master of science in Computer engineering.Google Scholar
- Rashwan, M., Sallab, A. Al, Raafat, H. and Rafea, A. 2014. Automatic Arabic diacritics restoration based on deep nets. Proceedings of the EMNLP 2014 Workshop on Arabic Natural Langauge Processing (ANLP), Doha, Qatar, October 25 (2014), 65--72.Google Scholar
- Said, A., El-sharqwi, M., Chalabi, A. and Kamal, E. 2013. A Hybrid Approach for Arabic Diacritization. 18th International Conference on Applications of Natural Language to Information Systems, NLDB 2013, Salford, UK, June 19--21, 2013 (2013), 53--64.Google Scholar
- Shaalan, K. 2010. Rule-based Approach in Arabic Natural Language Processing. International Journal on Information and Communication Technologies. 3, 3 (2010), 11--19.Google Scholar
- Shaalan, K., Abo Bakr, H.M. and Ziedan, I. 2009. A Arabic Text Diacritization: Overview And Solution hybrid approach for building Arabic diacritizer. Proceedings of the EACL 2009 Workshop on Computational Approaches to Semitic Languages, Athens, Greece, 31 March (2009), 27--35.Google Scholar
- Shahrour, A., Khalifa, S. and Habash, N. 2015. Improving Arabic Diacritization through Syntactic Analysis. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, SCA2019, October 2-4, 2019, Casablanca, Morocco EMNLP, Lisbon, Portugal, 17--21 September (2015), 1309--1315.Google Scholar
- Zayyan, A.A., Elmahdy, M., binti Husni, H. and Al Ja'am, J.M. 2016. Automatic Diacritics Restoration for Dialectal Arabic Text. International Journal of Computing and Information Sciences. 12, 2 (2016), 159--165. Google ScholarCross Ref
Recommendations
Arabic Diacritization with Gated Recurrent Unit
LOPAL '18: Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and ApplicationsArabic and similar languages require the use of diacritics in order to determine the necessary parameters to pronounce and identify every part of the speech correctly. Therefore, when it comes to perform Natural Language Processing (NLP) over Arabic, ...
Automatic diacritization of Arabic text using recurrent neural networks
This paper presents a sequence transcription approach for the automatic diacritization of Arabic text. A recurrent neural network is trained to transcribe undiacritized Arabic text with fully diacritized sentences. We use a deep bidirectional long short-...
Hybrid HMM/DNN System for Arabic Handwriting Keyword Spotting
Image Analysis and RecognitionAbstractDeep Neural Networks (DNN) have been incorporated in handwriting recognition systems and have proven their effectiveness. This paper presents an efficient hybrid word spotting system for Arabic handwritten documents. We investigate the optimal ...
Comments