@inproceedings{saeed-etal-2026-maqam,
title = "{MAQAM}: A Richly Annotated Corpus for {Q}uranic {A}rabic Morphological Analysis and Disambiguation",
author = "Saeed, Mostafa and
Amjad, Mohammad and
Kassem, Mahmoud and
Bondok, Rawan and
Nassar, Mayar and
Mahmoud Wizani, Adel and
Elgamal, Salman and
Reda, Mahmoud and
Habash, Nizar",
editor = "Abdul-Mageed, Muhammad and
Elsayed, Tamer and
Bouamor, Houda and
El-Beltagy, Samhaa and
Al-Khalifa, Hend and
Abdelali, Ahmed and
Elmadany, AbdelRahim and
El Mekki, Abdellah and
Darwish, Kareem and
Alkhereyf, Sakhar and
Luqman, Hamzah and
Khalifa, Salam and
Magdy, Samar and
Alyafeai, Zaid and
Hamed, Injy and
Zaghouani, Wajdi and
Zitouni, Imed",
booktitle = "Proceedings of The Fourth {A}rabic Natural Language Processing Conference",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.arabicnlp-main.11/",
pages = "151--163",
ISBN = "979-8-89176-473-6",
abstract = "Quranic Arabic poses important challenges for Arabic NLP due to its distinctive morphology, syntax, and orthography. We present MAQAM, a richly annotated Morphosyntactic Arabic Quranic Analysis and Modeling resource covering the entire Quran, providing 22 lexical, inflectional, and clitic features for every word. The resource is constructed through a semi-automatic pipeline combining feature enrichment, morphological synchronization, and expert manual annotation and validation. We further evaluate Quranic morphosyntactic disambiguation under a realistic setting where the input may be represented in either the Uthmani or Imla{'}i script, and investigate cross-domain transfer between Quranic Arabic and MSA. Our results show that MSA and Quranic resources are complementary for Quranic disambiguation, with the best performance obtained by combining MSA training data, Quran-specific analyzers, and both script representations, whereas for MSA, only integrating Quranic resources provide modest gains. We make MAQAM publicly available."
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<abstract>Quranic Arabic poses important challenges for Arabic NLP due to its distinctive morphology, syntax, and orthography. We present MAQAM, a richly annotated Morphosyntactic Arabic Quranic Analysis and Modeling resource covering the entire Quran, providing 22 lexical, inflectional, and clitic features for every word. The resource is constructed through a semi-automatic pipeline combining feature enrichment, morphological synchronization, and expert manual annotation and validation. We further evaluate Quranic morphosyntactic disambiguation under a realistic setting where the input may be represented in either the Uthmani or Imla’i script, and investigate cross-domain transfer between Quranic Arabic and MSA. Our results show that MSA and Quranic resources are complementary for Quranic disambiguation, with the best performance obtained by combining MSA training data, Quran-specific analyzers, and both script representations, whereas for MSA, only integrating Quranic resources provide modest gains. We make MAQAM publicly available.</abstract>
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%0 Conference Proceedings
%T MAQAM: A Richly Annotated Corpus for Quranic Arabic Morphological Analysis and Disambiguation
%A Saeed, Mostafa
%A Amjad, Mohammad
%A Kassem, Mahmoud
%A Bondok, Rawan
%A Nassar, Mayar
%A Mahmoud Wizani, Adel
%A Elgamal, Salman
%A Reda, Mahmoud
%A Habash, Nizar
%Y Abdul-Mageed, Muhammad
%Y Elsayed, Tamer
%Y Bouamor, Houda
%Y El-Beltagy, Samhaa
%Y Al-Khalifa, Hend
%Y Abdelali, Ahmed
%Y Elmadany, AbdelRahim
%Y El Mekki, Abdellah
%Y Darwish, Kareem
%Y Alkhereyf, Sakhar
%Y Luqman, Hamzah
%Y Khalifa, Salam
%Y Magdy, Samar
%Y Alyafeai, Zaid
%Y Hamed, Injy
%Y Zaghouani, Wajdi
%Y Zitouni, Imed
%S Proceedings of The Fourth Arabic Natural Language Processing Conference
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-473-6
%F saeed-etal-2026-maqam
%X Quranic Arabic poses important challenges for Arabic NLP due to its distinctive morphology, syntax, and orthography. We present MAQAM, a richly annotated Morphosyntactic Arabic Quranic Analysis and Modeling resource covering the entire Quran, providing 22 lexical, inflectional, and clitic features for every word. The resource is constructed through a semi-automatic pipeline combining feature enrichment, morphological synchronization, and expert manual annotation and validation. We further evaluate Quranic morphosyntactic disambiguation under a realistic setting where the input may be represented in either the Uthmani or Imla’i script, and investigate cross-domain transfer between Quranic Arabic and MSA. Our results show that MSA and Quranic resources are complementary for Quranic disambiguation, with the best performance obtained by combining MSA training data, Quran-specific analyzers, and both script representations, whereas for MSA, only integrating Quranic resources provide modest gains. We make MAQAM publicly available.
%U https://aclanthology.org/2026.arabicnlp-main.11/
%P 151-163
Markdown (Informal)
[MAQAM: A Richly Annotated Corpus for Quranic Arabic Morphological Analysis and Disambiguation](https://aclanthology.org/2026.arabicnlp-main.11/) (Saeed et al., ArabicNLP 2026)
ACL
- Mostafa Saeed, Mohammad Amjad, Mahmoud Kassem, Rawan Bondok, Mayar Nassar, Adel Mahmoud Wizani, Salman Elgamal, Mahmoud Reda, and Nizar Habash. 2026. MAQAM: A Richly Annotated Corpus for Quranic Arabic Morphological Analysis and Disambiguation. In Proceedings of The Fourth Arabic Natural Language Processing Conference, pages 151–163, Budapest, Hungary. Association for Computational Linguistics.