TY - GEN
T1 - Arabic Text Diacritization Using Deep Neural Networks
AU - Fadel, Ali
AU - Tuffaha, Ibraheem
AU - Al-Jawarneh, Bara
AU - Al-Ayyoub, Mahmoud
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - Diacritization of Arabic text is both an interesting and a challenging problem at the same time with various applications ranging from speech synthesis to helping students learning the Arabic language. Like many other tasks or problems in Arabic language processing, the weak efforts invested into this problem and the lack of available (open-source) resources hinder the progress towards solving this problem. This work provides a critical review for the currently existing systems, measures and resources for Arabic text diacritization. Moreover, it introduces a much-needed free-for-all cleaned dataset that can be easily used to benchmark any work on Arabic diacritization. Extracted from the Tashkeela Corpus, the dataset consists of 55K lines containing about 2.3M words. After constructing the dataset, existing tools and systems are tested on it. The results of the experiments show that the neural Shakkala system significantly outperforms traditional rule-based approaches and other closed-source tools with a Diacritic Error Rate (DER) of 2.88% compared with 13.78%, which the best DER for the non-neural approach (obtained by the Mishkal tool).
AB - Diacritization of Arabic text is both an interesting and a challenging problem at the same time with various applications ranging from speech synthesis to helping students learning the Arabic language. Like many other tasks or problems in Arabic language processing, the weak efforts invested into this problem and the lack of available (open-source) resources hinder the progress towards solving this problem. This work provides a critical review for the currently existing systems, measures and resources for Arabic text diacritization. Moreover, it introduces a much-needed free-for-all cleaned dataset that can be easily used to benchmark any work on Arabic diacritization. Extracted from the Tashkeela Corpus, the dataset consists of 55K lines containing about 2.3M words. After constructing the dataset, existing tools and systems are tested on it. The results of the experiments show that the neural Shakkala system significantly outperforms traditional rule-based approaches and other closed-source tools with a Diacritic Error Rate (DER) of 2.88% compared with 13.78%, which the best DER for the non-neural approach (obtained by the Mishkal tool).
KW - Arabic text diacritization
KW - Deep Learning
KW - Deep Neural Network
UR - https://www.scopus.com/pages/publications/85073888745
U2 - 10.1109/CAIS.2019.8769512
DO - 10.1109/CAIS.2019.8769512
M3 - Conference contribution
AN - SCOPUS:85073888745
T3 - 2nd International Conference on Computer Applications and Information Security, ICCAIS 2019
BT - 2nd International Conference on Computer Applications and Information Security, ICCAIS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Computer Applications and Information Security, ICCAIS 2019
Y2 - 1 May 2019 through 3 May 2019
ER -