Direct text classifier for thematic Arabic discourse documents
Joint Authors
al-Khatib, Raid
al-Shunnaq, Muawiyah
Daradikah, Muhammad
Malkawi, Rami
Nahar, Khalid
Source
The International Arab Journal of Information Technology
Issue
Vol. 17, Issue 3 (31 May. 2020), pp.394-403, 10 p.
Publisher
Zarqa University Deanship of Scientific Research
Publication Date
2020-05-31
Country of Publication
Jordan
No. of Pages
10
Main Subjects
Information Technology and Computer Science
Abstract EN
Maintaining the topical coherence while writing a discourse is a major challenge confronting novice and non-novice writers alike.
This challenge is even more intense with Arabic discourse because of the complex morphology and the widespread of synonyms in Arabic language.
In this research, we present a direct classification of Arabic discourse document while writing.
This prescriptive proposed framework consists of the following stages: data collection, pre-processing, construction of Language Model (LM), topics identification, topics classification, and topic notification.
To prove and demonstrate our proposed framework, we designed a system and applied it on a corpus of 2800 Arabic discourse documents synthesized into four predefined topics related to: Culture, Economy, Sport, and Religion.
System performance was analysed, in terms of accuracy, recall, precision, and F-measure.
The results demonstrated that the proposed topic modeling-based decision framework is able to classify topics while writing a discourse with accuracy of 91.0%.
American Psychological Association (APA)
Nahar, Khalid& al-Khatib, Raid& al-Shunnaq, Muawiyah& Daradikah, Muhammad& Malkawi, Rami. 2020. Direct text classifier for thematic Arabic discourse documents. The International Arab Journal of Information Technology،Vol. 17, no. 3, pp.394-403.
https://search.emarefa.net/detail/BIM-962353
Modern Language Association (MLA)
Nahar, Khalid…[et al.]. Direct text classifier for thematic Arabic discourse documents. The International Arab Journal of Information Technology Vol. 17, no. 3 (May. 2020), pp.394-403.
https://search.emarefa.net/detail/BIM-962353
American Medical Association (AMA)
Nahar, Khalid& al-Khatib, Raid& al-Shunnaq, Muawiyah& Daradikah, Muhammad& Malkawi, Rami. Direct text classifier for thematic Arabic discourse documents. The International Arab Journal of Information Technology. 2020. Vol. 17, no. 3, pp.394-403.
https://search.emarefa.net/detail/BIM-962353
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 400-402
Record ID
BIM-962353