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SANAD: Single-label Arabic News Articles Dataset for automatic text categorization.

Omar Einea¹, Ashraf Elnagar¹, Ridhwan Al Debsi¹

¹University of Sharjah, United Arab Emirates.

|August 24, 2019

Summary

Researchers created the Single-labeled Arabic News Articles Dataset (SANAD) to advance Arabic text classification. This large dataset aids Natural Language Processing (NLP) tasks for Modern Standard Arabic.

Keywords:

Arabic Natural language processing News articles Single-label text classification

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Area of Science:

Natural Language Processing (NLP)
Computational Linguistics
Arabic Text Classification

Background:

Arabic text classification lacks sufficient training resources.
Existing datasets are not representative enough for robust model training.

Discussion:

The Single-labeled Arabic News Articles Dataset (SANAD) addresses the resource scarcity for Arabic NLP.
SANAD comprises nearly 200,000 articles from three major news portals, categorized into seven classes.
The dataset is provided in raw format to facilitate diverse research applications.

Key Insights:

SANAD offers a substantial, single-labeled dataset crucial for developing Arabic text classifiers.
The availability of SANAD is expected to significantly boost research in Arabic NLP, particularly for text categorization.
This resource supports various NLP tasks involving Modern Standard Arabic (MSA) text.

Outlook:

Facilitate the development of more accurate Arabic text classification models.
Encourage further research in Arabic Natural Language Processing and computational linguistics.
Serve as a benchmark for future Arabic NLP dataset creation and evaluation.