A Hybrid Approach for Automatic Morphological Diacritization of Arabic Text
M Noaman, H., S. Sarhan, S., Rashwan, M. (2018). A Hybrid Approach for Automatic Morphological Diacritization of Arabic Text. EKB Journal Management System, 14(2), 39-46. doi: 10.21608/mjcis.2018.312008
Hatem M Noaman; Shahenda S. Sarhan; M. A. A. Rashwan. "A Hybrid Approach for Automatic Morphological Diacritization of Arabic Text". EKB Journal Management System, 14, 2, 2018, 39-46. doi: 10.21608/mjcis.2018.312008
M Noaman, H., S. Sarhan, S., Rashwan, M. (2018). 'A Hybrid Approach for Automatic Morphological Diacritization of Arabic Text', EKB Journal Management System, 14(2), pp. 39-46. doi: 10.21608/mjcis.2018.312008
M Noaman, H., S. Sarhan, S., Rashwan, M. A Hybrid Approach for Automatic Morphological Diacritization of Arabic Text. EKB Journal Management System, 2018; 14(2): 39-46. doi: 10.21608/mjcis.2018.312008

A Hybrid Approach for Automatic Morphological Diacritization of Arabic Text

Mansoura Journal for Computer and Information Sciences
Volume 14, Issue 2, December 2018, Page 39-46  XML PDF (557.72 K)
Document Type: Original Research Articles.
DOI: 10.21608/mjcis.2018.312008
View on SCiNiTO View on SCiNiTO
Authors
Hatem M Noaman1; Shahenda S. Sarhan1; M. A. A. Rashwan2
1Computer Science Department, Mansoura University, Egypt
2Electronics and Communications Department, Cairo University, Egypt
Abstract
 Arabic Modern texts are commonly written without diacritization, which is a critical task for other Arabic
processing tasks as word sense disambiguation, automatic speech recognition, and text to speech, where word meaning
or pronunciation is decided based on the diacritic signs assigned to each letter. This paper presents a novel approach for automatic Arabic text diacritization using deep encode-decode recurrent neural networks that is followed by several text correction techniques, to improve the overall system output accuracy. Experimental results of the proposed system on Wikinews test set show superior performance and are competitive with those of the-state-of-the-art diacritization methods. Namely, our method achieves morphological diacritization Word Error Rate (WER) 3.85% and Diacritic Error Rate (DER) 1.12% 
Keywords
Arabic Natural Language Processing; Automatic Morphological Diacritization; deep encode-decode recurrent neural networks
Statistics
Article View: 28
PDF Download: 29