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MADOran: A morphologically annotated dataset of Oran.

Majdi Sawalha1,2, Faisal Al-Shargi3, Sane Yagi4,5

  • 1King Abdullah II School of Information Technology, The University of Jordan, Amman, Jordan.

Data in Brief
|October 27, 2025
PubMed
Summary

A new dataset, MADOran, offers detailed morphological annotations for Orani Arabic (ORN), aiding NLP tasks like machine translation and dialect identification. This resource supports linguistic research and Arabic language learning.

Keywords:
DialectDialect identificationManually annotatedMorphological analysisOrani-Arabic

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Area of Science:

  • Computational Linguistics
  • Natural Language Processing
  • Arabic Dialectology

Background:

  • Limited annotated resources exist for Arabic dialects, hindering NLP development.
  • Orani Arabic (ORN) presents unique morphological and syntactic features requiring specialized datasets.
  • Existing annotation tools and guidelines need adaptation for dialectal Arabic.

Purpose of the Study:

  • Introduce MADOran, a novel morphologically annotated dataset for Orani Arabic.
  • Provide a valuable resource for NLP applications and linguistic research on Arabic dialects.
  • Facilitate the development of dialect-specific tools and resources for ORN.

Main Methods:

  • Collected 30,919 words from diverse written and spoken genres of Orani Arabic.
  • Manually annotated each word with a fine-grained tagset (POS, root, pattern, translations).
  • Utilized the Dialectal Word Annotation Tool for Arabic (DIWAN), adapting guidelines from The Dynamic Arabella Corpus (Arabella).

Main Results:

  • Developed MADOran, a comprehensive dataset with detailed morphological annotations for ORN.
  • The dataset covers various topics and conversational contexts, reflecting real-world language use.
  • Annotations include part-of-speech, root, pattern, and English/French translations.

Conclusions:

  • MADOran is a significant resource for advancing Arabic dialect processing, particularly for ORN.
  • The dataset enables training language models and supports comparative linguistic studies with Modern Standard Arabic (MSA).
  • MADOran adheres to data stewardship principles, ensuring findability, accessibility, interoperability, and reusability.