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