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Compression-Based Parts-of-Speech Tagger for The Arabic Language
Ibrahim S. Alkhazi, William J. Teahan
Pages - 1 - 15     |    Revised - 31-03-2019     |    Published - 30-04-2019
Volume - 10   Issue - 1    |    Publication Date - April 2019  Table of Contents
Natural Language Processing, Arabic Part-of-Speech Tagger, Hidden Markov Model, Statistical Language Model.
This paper explores the use of Compression-based models to train a Part-of-Speech (POS) tagger for the Arabic language. The newly developed tagger is based on the Prediction-by-Partial Matching (PPM) compression system, which has already been employed successfully in several NLP tasks. Several models were trained for the new tagger, the first models were trained using a silver-standard data from two different POS Arabic taggers, and the second model utilised the BAAC corpus, which is a 50K term manually annotated MSA corpus, where the PPM tagger achieved an accuracy of 93.07%. Also, the tag-based models were utilised to evaluate the performance of the new tagger by first tagging different Classical Arabic corpora and Modern Standard Arabic corpora then compressing the text using tag-based compression models. The results show that the use of silver-standard models has led to a reduction in the quality of the tag-based compression by an average of 0.43%, whereas the use of the gold-standard model has increased the tag-based compression quality by an average of 4.61% when used to tag Modern Standard Arabic text.
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Mr. Ibrahim S. Alkhazi
College of Computers & Information Technology Tabuk University Tabuk, Saudi Arabia - Saudi Arabia
Dr. William J. Teahan
School of Computer Science and Electronic Engineering Bangor University United Kingdom - United Kingdom

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