The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Metadata Harvesting (OAI2)
  • Digital Archiving Policy
  • Promote your Publication

IJACSA

  • About the Journal
  • Call for Papers
  • Author Guidelines
  • Fees/ APC
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Editors
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors

Future of Information and Communication Conference (FICC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Guidelines
  • Fees
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Editors
  • Reviewers
  • Subscribe

Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Classifying and Segmenting Classical and Modern Standard Arabic using Minimum Cross-Entropy

Author 1: Ibrahim S Alkhazi
Author 2: William J. Teahan

Download PDF

Digital Object Identifier (DOI) : 10.14569/IJACSA.2017.080456

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 8 Issue 4, 2017.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: Text classification is the process of assigning a text or a document to various predefined classes or categories to reflect their contents. With the rapid growth of Arabic text on the Web, studies that address the problems of classification and segmentation of the Arabic language are limited compared to other languages, most of which implement word-based and feature extraction algorithms. This paper adopts a PPM character-based compression scheme to classify and segment Classical Arabic (CA) and Modern Standard Arabic (MSA) texts. An initial experiment using the PPM classification method on samples of text resulted in an accuracy of 95.5%, an average precision of 0.958, an average recall of 0.955 and an average F-measure of 0.954, using the concept of minimum cross-entropy. PPM-based classification experiments on standard Arabic corpora showed that they contained different types of text (CA or MSA), or a mixture of the both (CA and MSA). Further experiments with the same corpora showed that a more accurate picture of the contents of the corpora was possible using the PPM-based segmentation method. Tag-based compression experiments (using tags produced by parts-of-speech Arabic taggers) also showed that the quality of the tagging (as measured by compression quality) is significantly affected when tagging either CA and MSA text. The conclusion is that NLP applications (such as taggers) should treat these texts separately and use different training data for each or process them differently.

Keywords: text classification; Arabic language; Classical Arabic; Modern Standard Arabic

Ibrahim S Alkhazi and William J. Teahan, “Classifying and Segmenting Classical and Modern Standard Arabic using Minimum Cross-Entropy” International Journal of Advanced Computer Science and Applications(IJACSA), 8(4), 2017. http://dx.doi.org/10.14569/IJACSA.2017.080456

@article{Alkhazi2017,
title = {Classifying and Segmenting Classical and Modern Standard Arabic using Minimum Cross-Entropy},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.080456},
url = {http://dx.doi.org/10.14569/IJACSA.2017.080456},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {4},
author = {Ibrahim S Alkhazi and William J. Teahan}
}


IJACSA

Upcoming Conferences

Future of Information and Communication Conference (FICC) 2023

2-3 March 2023

  • Virtual

Computing Conference 2023

22-23 June 2023

  • London, United Kingdom

IntelliSys 2023

7-8 September 2023

  • Amsterdam, The Netherlands

Future Technologies Conference (FTC) 2023

2-3 November 2023

  • San Francisco, United States
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. Registered in England and Wales. Company Number 8933205. All rights reserved. thesai.org