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MLAR: Machine Learning based System for Measuring the Readability of Online Arabic News

by Mohammed M. Fouad, Marwa A. Atyah
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 154 - Number 6
Year of Publication: 2016
Authors: Mohammed M. Fouad, Marwa A. Atyah
10.5120/ijca2016912160

Mohammed M. Fouad, Marwa A. Atyah . MLAR: Machine Learning based System for Measuring the Readability of Online Arabic News. International Journal of Computer Applications. 154, 6 ( Nov 2016), 29-33. DOI=10.5120/ijca2016912160

@article{ 10.5120/ijca2016912160,
author = { Mohammed M. Fouad, Marwa A. Atyah },
title = { MLAR: Machine Learning based System for Measuring the Readability of Online Arabic News },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2016 },
volume = { 154 },
number = { 6 },
month = { Nov },
year = { 2016 },
issn = { 0975-8887 },
pages = { 29-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume154/number6/26497-2016912160/ },
doi = { 10.5120/ijca2016912160 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:59:32.268625+05:30
%A Mohammed M. Fouad
%A Marwa A. Atyah
%T MLAR: Machine Learning based System for Measuring the Readability of Online Arabic News
%J International Journal of Computer Applications
%@ 0975-8887
%V 154
%N 6
%P 29-33
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Online news became one of the favorite information sources for most of the people nowadays because of its update rate and availability over the 24 hours rather than the traditional newspapers. Measuring the readability of the news articles gives a clear view for both the readers and the writers about how easily people can read and understand these articles. In this paper, we present MLAR, a new machine learning based system for Arabic text readability, and use it in measuring the readability of the Arabic online news articles from different outlets. The proposed system is able to determine the topic of each article efficiently and calculates its readability score level. The results show that readability of the online Arabic news is affected by the nature of its topic and the source outlet. The writing style of news articles in each topic differs from one outlet to another.

References
  1. Dalecki, L., Lasorsa, D. L., and Lewis, S. C. 2009. The News Readability Problem, Journalism Practice, vol. 3(1), pp. 1-12.
  2. Al-Khalifa, H. S., and Al-Ajlan A. A. 2010. Automatic Readability Measurements of the Arabic Text: An Exploratory Study, the Arabian Journal for Science and Engineering, vol. 35(2C), pp. 103-124.
  3. Compton, D. L., Appleton, A. C., and Hosp, M. K. 2004. Exploring the relationship between text-leveling systems and reading accuracy and fluency in second-grade students who are average to poor decoders, Learning Disabilities Research & Practice, vol. 19, pp. 176-184.
  4. Bailin, A., and Grafstein, A. 2001. The linguistic assumptions underlying readability formulae: A critique, Language and Communication, vol. 21, pp. 285-301.
  5. Barbham, E. G., and Villaume, S. K. 2002. Leveled text: The good news and the bad news, The Reading Teacher, vol. 55, pp. 438-41.
  6. Persampieri, M., Gortmaker, V., Daly III, E. J., Sheridan, S. M., and McCurdy, M. 2006. Promoting parent use of empirically supported reading interventions: Two experimental investigations of child outcomes, Behavioral Interventions, vol. 21, pp. 31-57.
  7. Begeny, J. C., and Greene, D. J. 2014. Can readability formulas be used to successfully gauge difficulty of reading materials, Psychology in the Schools, vol. 51(2), pp. 198-215.
  8. Dale, E., and Chall, J. 1948. A formula for predicting readability: Instructions, Educational Research Bulletin, vol. 27, pp. 37-54.
  9. Flesch, R. 1948. A new readability yardstick, Journal of Applied Psychology, vol. 32, pp. 221-229.
  10. Gunning, R. 1952. The technique of clear writing. New York: McGraw-Hill.
  11. McLaughlin, G. H. 1969. SMOG grading: A new readability formula, Journal of Reading, vo. 22, pp. 639-646.
  12. Spache, G. 1953. A new readability formula for primary grade reading materials, The Elementary School Journal, vol. 53, pp. 410-413.
  13. Al-Heeti, K. 1984. Judgment analysis technique applied to readability prediction of Arabic reading material, Ph.D. Thesis, University of North Colorado.
  14. Al-Tamimi, A., Jaradat, M., Aljarrah, N., and Ghanim, S. 2014. AARI: Automatic Arabic Readability Index, The International Arab Journal of Information Technology, vol. 11(4), pp. 370-378.
  15. El-Haj, M., and Rayson, P. 2016. OSMAN – A Novel Arabic Readability Metric, Proceedings of the Language Resources and Evaluation Conference 2016. European Language Resources Association (ELRA), Slovenia, pp. 250-255.
  16. Mat Daud, N., Hassan, H., and Abdul Aziz, N. 2013. A Corpus-Based Readability Formula for Estimate of Arabic Texts Reading Difficulty, World Applied Sciences Journal, vol. 21, pp. 168-173.
  17. Al-Thubaity, A. O. 2015. A 700M+ Arabic corpus: KACST Arabic corpus design and construction, Language Resources and Evaluation, vol. 49(3), pp. 721-751.
  18. Saad, M. K., and Ashour, W. 2010. OSAC: Open Source Arabic Corpus, Proceedings of the 6th International Conference on Electrical and Computer Systems (EECS’10), Lefke, North Cyprus, pp. 1-6.
  19. RapidMiner® Data Science Tool: https://rapidminer.com/
  20. The Stanford Natural Language Processing Group, Stanford NLP: http://nlp.stanford.edu/software/
  21. The Arabic WordNet Project: http://globalwordnet.org/arabic-wordnet/
Index Terms

Computer Science
Information Sciences

Keywords

MLAR Machine Learning Text Mining Readability Arabic Online News Natural Language Processing.

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