Proper Noun Diacritization for Arabic Wikipedia: A Benchmark Dataset
Bondok, Rawan Nassar, Mayar Khalifa, Salam Micallef, Kurt Habash, Nizar
Bondok, Rawan
Nassar, Mayar
Khalifa, Salam
Micallef, Kurt
Habash, Nizar
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Department
Natural Language Processing
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Conference proceeding
Date
2025
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Language
English
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Abstract
Proper nouns in Arabic Wikipedia are frequently undiacritized, creating ambiguity in pronunciation and interpretation, especially for transliterated named entities of foreign origin. While transliteration and diacritization have been well-studied separately in Arabic NLP, their intersection remains underexplored. In this paper, we introduce a new manually diacritized dataset of Arabic proper nouns of various origins with their English Wikipedia equivalent glosses, and present the challenges and guidelines we followed to create it. We benchmark GPT-4o on the task of recovering full diacritization given the undiacritized Arabic and English forms, and analyze its performance. Achieving 73% accuracy, our results underscore both the difficulty of the task and the need for improved models and resources. We release our dataset to facilitate further research on Arabic Wikipedia proper noun diacritization.
Citation
R. Bondok, M. Nassar, S. Khalifa, K. Micallef, and N. Habash, “Proper Noun Diacritization for Arabic Wikipedia: A Benchmark Dataset,” Proceedings of the 2nd Workshop on Advancing Natural Language Processing for Wikipedia (WikiNLP 2025), pp. 31–44, 2025, doi: 10.18653/V1/2025.WIKINLP-1.8
Source
Proceedings of the 2nd Workshop on Advancing Natural Language Processing for Wikipedia (WikiNLP 2025)
Conference
2nd Workshop on Advancing Natural Language Processing for Wikipedia (WikiNLP 2025)
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Source
2nd Workshop on Advancing Natural Language Processing for Wikipedia (WikiNLP 2025)
Publisher
Association for Computational Linguistics