PART OF SPEECH TAGGER FOR ARABIC TEXT BASED SUPPORT VECTOR MACHINES: A REVIEW

Abstract
There is not much research that discusses the Part of speech (POS) tagger for the Arabic language. Hence, the Arabic language is challenging to identify the types of part of the speech of a particular word in a given context because most modern texts do not use diacritical marks. Hence, one word could spell in several different ways. Also, the distinction between the differences in the Arab derivatives is a complicated issue, so the clarification of the correct types on the POS requires the use of different resources and advanced processing. Therefore, the study of part of the speech can contribute to literature and progress in the signs of the Arabic language. The POS is employed in different fields of natural languages processing such as text translation, and extraction, text classification and identifies the type of speech. Identifying unique POS tags for the Arabic language is a difficult task. This paper aims to review the implementation of support vector machines (SVM) for utilizing the POS for the Arabic Language. Therefore, the primary objectives of this paper are to summarize and organize the works for tagging the Arabic text based on SVM automatically and efficiently for motivating and guiding researchers to do more research on the online applications for the Arabic language.

Authors
Jabar H Yousif, Maryam H Al-Risi
Sohar University, Oman

Keywords
Part of Speech, Arabic Text Tagging, SVM, NLP, Machine Learning, Corpus
Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 9 , Issue: 2 )
Date of Publication :
Januray 2019
DOI :

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.