Accurate and fast recurrent neural network solution for the automatic diacritization of Arabic text
Other Title(s)
حل دقيق و سريع لوضع الحركات المميزة على نصوص اللغة العربية باستخدام الشبكات العصبية المتكررة
Joint Authors
Abandah, Ghayth A.
Abd al-Karim, Asma
Source
Jordanian Journal of Computetrs and Information Technology
Issue
Vol. 6, Issue 2 (30 Jun. 2020), pp.103-121, 19 p.
Publisher
Princess Sumaya University for Technology
Publication Date
2020-06-30
Country of Publication
Jordan
No. of Pages
19
Main Subjects
Information Technology and Computer Science
Abstract EN
Arabic is mostly written now without its diacritics (short vowels).
Adding these diacritics decreases reading ambiguity among other benefits.
This work aims to develop a fast and accurate machine learning solution to diacritize Arabic text automatically.
This paper uses long short-term memory (LSTM) recurrent neural networks to diacritize Arabic text.
Intensive experiments are performed to evaluate proposed alternative design and data encoding options towards a fast and accurate solution.
Our experiments involve investigating and handling problems in sequence lengths, proposing and evaluating alternative encodings of the diacritized output sequences and tuning and evaluating neural network options including architecture, network size and hyper-parameters.
This paper recommends a solution that can be fast trained on a large dataset and uses four bidirectional LSTM layers to predict the diacritics of the input sequence of Arabic letters.
This solution achieves a diacritization error rate of 2.46% on the LDC ATB3 dataset benchmark and 1.97% on the larger new Tashkeela dataset.
This latter rate is 47% improvement over the best-published previous result.
American Psychological Association (APA)
Abandah, Ghayth A.& Abd al-Karim, Asma. 2020. Accurate and fast recurrent neural network solution for the automatic diacritization of Arabic text. Jordanian Journal of Computetrs and Information Technology،Vol. 6, no. 2, pp.103-121.
https://search.emarefa.net/detail/BIM-1416227
Modern Language Association (MLA)
Abandah, Ghayth A.& Abd al-Karim, Asma. Accurate and fast recurrent neural network solution for the automatic diacritization of Arabic text. Jordanian Journal of Computetrs and Information Technology Vol. 6, no. 2 (Jun. 2020), pp.103-121.
https://search.emarefa.net/detail/BIM-1416227
American Medical Association (AMA)
Abandah, Ghayth A.& Abd al-Karim, Asma. Accurate and fast recurrent neural network solution for the automatic diacritization of Arabic text. Jordanian Journal of Computetrs and Information Technology. 2020. Vol. 6, no. 2, pp.103-121.
https://search.emarefa.net/detail/BIM-1416227
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 119-121
Record ID
BIM-1416227