Abstract: The availability of a machine translation to translate from English-to-Arabic with high accuracy is not available because of the difficult morphology of the Arabic Language. A hybrid machine translation system between Example Based machine translation technique and Translation memory was introduced in this paper. Two datasets have been used in the experiments that were constructed by using internal medicine publications and Worldwide Arabic Medical Translation Guide Common Medical Terms sorted by Arabic. To examine the accuracy of the system constructed four experiments were made using Example Based Machine Translation system in the first, Google Translate in the second and Example Based with Google translate in the third and the fourth is the system proposed using Example Based with Translation memory. The system constructed achieved 77.17 score for the first dataset and 63.85 score for the second which were the highest score using BLEU score.

Rana Ehab, Eslam Amer and Mahmoud Gadallah, “English-Arabic Hybrid Machine Translation System using EBMT and Translation Memory” International Journal of Advanced Computer Science and Applications(IJACSA), 10(1), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100126

@article{Ehab2019,
title = {English-Arabic Hybrid Machine Translation System using EBMT and Translation Memory},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100126},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100126},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {1},
author = {Rana Ehab and Eslam Amer and Mahmoud Gadallah}
}