International Journal of Computation and Applied Sciences IJOCAAS
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Hidden Markov Model Tagger for Applications Based Arabic Text: A review
Jabar H. Yousif
The immense increase in the use of the Arabic Language in transmitting information on the internet makes the Arabic Language a focus of researchers and commercial developers. The developing of an efficient Arabic POS tagger is not an easy task due to the complexity of the Language itself and the challenges of tagging disambiguation and unknown words. This paper aims to explore and review the use of Part of speech Tagger for Arabic text based on Hidden Markov Model. Besides, it is discussed and explored the implementation of POS tagger for different languages. This study examined a group of research papers that applied the Part of Speech to Arabic using the Hidden Markov Model. The results have shown that a large number of researchers achieved high accuracy rates in the classification of parts of speech correctly. Handi and Alshamsi achieved a high accuracy rate of 97.6% and 97.4% respectively. Kadim obtained an average accuracy of 75.38% for a Parallel Hidden Markov Model.
Neural Computing based Part of Speech Tagger for Arabic Language: A review study
Jabar H. Yousif
this paper aims to explore the implementation of part of speech tagger (POS) for Arabic Language using neural computing. The Arabic Language is one of the most important languages in the world. More than 422 million people use the Arabic Language as the primary media for writing and speaking. The part of speech is one crucial stage for most natural languages processing. Many factors affect the performance of POS including the type of language, the corpus size, the tag-set, the computation model. The artificial neural network (ANN) is modern paradigms that simulate the human behavior to learn, test and generalize the solutions. It maps the non-linear function into a simple linear model. Several researchers implemented the POS using ANN. This work proves that the using of ANN in utilizing the POS is achieving very well results. The performance has based the rate of accuracy, which most of the proposed models were obtained high accuracy between 90% and 99%. Besides, the using of neural models required less number of tag-sets for training and testing of the model. Most of NLP applications required accurate and fast POS, which is offered by the neural model.