TY  - JOUR
T1  - Design and Implement an Automatic Neural Tagger Based Arabic Language for NLP Applications
AU - , Jabar, H. Yousif AU - , Tengku, M. AU - , Tengku Sembok 
JO  - Asian Journal of Information Technology
VL  - 5
IS  - 7
SP  - 784
EP  - 789
PY  - 2006
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2006.784.789
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2006.784.789
KW  - Arabic part-of-speech
KW  -multilayer perceptron
KW  -neural networks
KW  -information coding
KW  -NLP
AB  - To this day, various methods such as the statistical model, rule-based models and Support Vector Machine have been used to implement the POS tagger systems. However, these approaches require a large amount of data in order to adapt and implement the POS tagger. The neural approaches, on the other hand, only use lesser amount of data to perform the training and learning stages. The Arabic part of speech (POS) based multilayer perceptron is designed and implemented, while the Error back-propagation learning algorithm is used. The experiments have proven that not only the multilayer perceptron tagger is highly accurate (of 99.99%), it requires low processing time and a lesser amount of data to achieve the learning phase. A new coding scheme is investigated and implemented.
ER  -