A scalable shallow learning approach for tagging Arabic news articles
Joint Authors
al-Qadi, Lin
al-Rifai, Hudhayfah
Ubayd, Safa
al-Najjar, Ashraf Y.
Source
Jordanian Journal of Computetrs and Information Technology
Issue
Vol. 6, Issue 3 (30 Sep. 2020), pp.263-280, 18 p.
Publisher
Princess Sumaya University for Technology
Publication Date
2020-09-30
Country of Publication
Jordan
No. of Pages
18
Main Subjects
Information Technology and Computer Science
Abstract EN
Text classification is the process of automatically tagging a textual document with the most relevant set of labels.
the aim of this work is to automatically tag an input document based on its vocabulary features.
to achieve this goal, two large datasets have been constructed from various Arabic news portals.
the first dataset consists of 90k single-labeled articles from 4 domains (business, middle east, technology and sports).
the second dataset has over 290k multi-tagged articles.
the datasets shall be made freely available to the research community on Arabic computational linguistics.
to examine the usefulness of both datasets, we implemented an array of ten shallow learning classifiers.
in addition, we implemented an ensemble model to combine best classifiers together in a majority-voting classifier.
the performance of the classifiers on the first dataset ranged between 87.7% (ada-boost) and 97.9% (SVM).
analyzing some of the misclassified articles confirmed the need for a multi-label opposed to single-label categorization for better classification results.
we used classifiers that were compatible with multi-labeling tasks, such as logistic regression and xgboost.
we tested the multi-label classifiers on the second larger dataset.
a custom accuracy metric, designed for the multi-labeling task, has been developed for performance evaluation along with hamming loss metric.
xgboost proved to be the best multi-labeling classifier, scoring an accuracy of 91.3%, higher than the logistic regression score of 87.6%.
American Psychological Association (APA)
al-Qadi, Lin& al-Rifai, Hudhayfah& Ubayd, Safa& al-Najjar, Ashraf Y.. 2020. A scalable shallow learning approach for tagging Arabic news articles. Jordanian Journal of Computetrs and Information Technology،Vol. 6, no. 3, pp.263-280.
https://search.emarefa.net/detail/BIM-1415640
Modern Language Association (MLA)
al-Rifai, Hudhayfah…[et al.]. A scalable shallow learning approach for tagging Arabic news articles. Jordanian Journal of Computetrs and Information Technology Vol. 6, no. 3 (Sep. 2020), pp.263-280.
https://search.emarefa.net/detail/BIM-1415640
American Medical Association (AMA)
al-Qadi, Lin& al-Rifai, Hudhayfah& Ubayd, Safa& al-Najjar, Ashraf Y.. A scalable shallow learning approach for tagging Arabic news articles. Jordanian Journal of Computetrs and Information Technology. 2020. Vol. 6, no. 3, pp.263-280.
https://search.emarefa.net/detail/BIM-1415640
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 277-279
Record ID
BIM-1415640