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A Large and Balanced Multi-Domain Arabic Corpus Annotated for Morphology, Syntax, and Readability

Summary

Arabic corpora have usually concentrated on one dimension: syntactic and morphological annotation, broad domain coverage, or readability labels. Few combine several linguistic layers with sampling balanced across genres and audiences. The authors build BAREC-10M, a tenfold expansion of the 1M-word BAREC readability corpus. It gathers 45 sub-corpora, including news, school curricula, literature, poetry, religious texts, benchmarks and child-directed media. Every document is tagged for domain (Arts & Humanities, Social Sciences, STEM), readership group (Foundational, Advanced, Specialized) and text category. Tokens are automatically annotated with rich morphology and UD-style dependency syntax, and sentences receive automatic readability on the 19-level BAREC scale. Children's material is scarce online, so the team hand-typed comics, magazines and book series from scans. Most sources are represented only by short excerpts, which serves both balance and fair use. The resource is released openly, and the authors compare it against fifteen earlier Arabic corpora. It matters because it gives one place to study how Arabic morphology, syntax and difficulty vary with audience and style.

Key points

  • BAREC-10M has about 10 million words drawn from 45 sub-corpora. It will be freely available for research and education at barec.camel-lab.com.
  • Annotation layers include diacritization, lemma, English gloss and POS. The morphological features annotated are proclitics, rationality, person, case, state, number, gender, mood, voice and aspect. The corpus also has UD dependency trees and lexical and sentence readability on the BAREC 1–19 scale, all produced automatically.
  • Manually digitized sources include:
    • 187 editions of The Five Adventurers;
    • 10 editions each of Batman, Superman and Lulu;
    • 74 Arab Youth Publisher books, 37 Reading Corner books and 35 Ladybird editions.
  • The corpus also contains 450 synthetic children's sentences generated with ChatGPT.
  • Several embedded subsets carry existing labels:
    • the full BAREC corpus, with manual readability;
    • part of CamelTB, with manual syntax;
    • DARES and the Tunisian and Emirati curricula, with grade levels;
    • ReadMe++, with CEFR levels.
  • Long works are typically sampled as their first 500 words, for example 2,758 Hindawi books and 596 Kalima books. Ofoq articles use their first 600 words.

Interesting ideas

  • I1. Capping each source at a short, fixed-length excerpt yields many diverse samples instead of a few huge ones, and keeps redistribution within fair use.
  • I2. The automatically annotated corpus deliberately embeds subsets with independent human labels (manual treebank, grade levels, CEFR), which act as anchors for checking the automatic layers.
  • I3. Tagging every document with domain × readership × genre allows linguistic and tool behaviour to be sliced by audience rather than averaged over everything.
  • I4. The team hand-digitized child-directed comics and magazines because web text badly underrepresents simple, narrative language.
  • I5. Readability is stored at both word level and sentence level next to morphology and syntax in one record. Difficulty therefore becomes a linguistic layer rather than a separate dataset.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (learn): Build English evaluation and silver-training pools from capped excerpts of many books and genres, so no single source dominates the error statistics.
  • I2 (learn): Mix gold UD and externally levelled English texts into the silver corpus and score the witness parsers' agreement on exactly those slices as a calibration check.
  • I3 (tree): Report perceptron-parser and gparse LAS separately for child-directed, general and specialized English, to see where the tree step breaks.
  • I4 (answer): Fable and tale QA would benefit from a curated child-level English sub-corpus, rather than assuming news-trained models transfer to simple stories.
  • I5 (tokens): Attach a lexical difficulty value to lemmas in crate en, so later stages and generation can explain or control how hard a text is.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

TopicsMorphologySemitic languagesSyntax & grammar formalisms

Cite

Cite as Khalid N. Elmadani, Adel Mahmoud Wizani, Hanada Taha Thomure and Nizar Habash. 2026. A Large and Balanced Multi-Domain Arabic Corpus Annotated for Morphology, Syntax, and Readability. Proceedings of the Fifteenth Language Resources and Evaluation Conference.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.63317/45f2o6t8piyi

@inproceedings{elmadani2026large,
  title = {{A Large and Balanced Multi-Domain Arabic Corpus Annotated for Morphology, Syntax, and Readability}},
  author = {Khalid N. Elmadani, Adel Mahmoud Wizani, Hanada Taha Thomure and Nizar Habash},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/45f2o6t8piyi},
  url = {https://doi.org/10.63317/45f2o6t8piyi}
}

References

52 references, 16 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Ahmed Abdelali, Kareem Darwish, Nadir Durrani, and Hamdy Mubarak. 2016. Farasa: A fast and furious segmenter for Arabic. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), pages 11–16, San Diego, California. our card
  2. Ibrahim Abu El-Khair. 2016. Abu el-khair corpus: A Modern Standard Arabic Corpus. International Journal of Recent Trends in Engineering & Research, 2:5–13. find
  3. Abbas Mahmoud Al-Akkad. 1938. Sarah. Hindawi. Sahih al-Imam Muhammad al Bukhari. 846. Bukhari. Dar Ibn Khathir. find
  4. Muhamed Al Khalil, Hind Saddiki, Nizar Habash, and Latifa Alfalasi. 2018. A leveled reading corpus of Modern Standard Arabic. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. European Language Resources Association (ELRA). find
  5. Bayan Al-Safadi. 2005. Al-Kashkoul: selection of poetry and prose for children   áÓ H@  P AJ m× :Èñº ‚ºË@  ). Al-Sa’ih ( ÈA ®£ CË Q  JË@ð Qª ‚Ë@  Library ( l ' A‚Ë@ éJ.JºÓ ). find
  6. Latifa Al-Sulaiti, Noorhan Abbas, Claire Brierley, Eric Atwell, and Ayman Alghamdi. 2016. Compilation of an Arabic children‘s corpus. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC‘16), pages 1808–1812, Portorož, Slovenia. European Language Resources Association (ELRA). our card
  7. Latifa Al-Sulaiti and Eric Steven Atwell. 2006. The design of a corpus of contemporary Arabic. International journal of corpus linguistics, 11(2):135– 171. find
  8. Abdulmohsen O Al-Thubaity. 2015. A 700m+ Arabic corpus: Kacst Arabic corpus design and construction. Language Resources and Evaluation, 49(3):721–751. find
  9. A. Alfaifi. 2015. Building the Arabic Learner Corpus and a System for Arabic Error Annotation. Ph.D. thesis, University of Leeds. find
  10. Bashar Alhafni, Nizar Habash, and Houda Bouamor. 2022. The Arabic parallel gender corpus 2.0: Extensions and analyses. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 1870–1884, Marseille, France. European Language Resources Association. our card
  11. Bashar Alhafni, Reem Hazim, Juan David Pineros Liberato, Muhamed Al Khalil, and Nizar Habash. 2024. The SAMER Arabic text simplification corpus. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 16079–16093, Torino, Italia. ELRA and ICCL. our card
  12. Sultan Almujaiwel, Damith Premasiri, Tharindu Ranasinghe, Mo El-Haj, and Ruslan Mitkov. 2025. Complex Concept-Based Readability Estimation from Arabic Curriculum. ACM Trans. Asian Low-Resour. Lang. Inf. Process. Just Accepted. find
  13. Shatha Altammami, Eric Atwell, and Ammar Alsalka. 2019. The Arabic–English parallel corpus of authentic hadith. International Journal on Islamic Applications in Computer Science And Technology-IJASAT. find
  14. Kinda Altarbouch, Khalid N. Elmadani, Ossama Obeid, Hanada Taha, and Nizar Habash. 2025. BAREC demo: Resources and tools for sentence-level Arabic readability assessment. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 950–959, Suzhou, China. Association for Computational Linguistics. find
  15. Tressy Arts, Yonatan Belinkov, Nizar Habash, Adam Kilgarriff, and Vit Suchomel. 2014. artenten: Arabic corpus and word sketches. Journal of King Saud University - Computer and Information Sciences, 26(4):357–371. Special Issue on Arabic NLP. find
  16. Yonatan Belinkov, Alexander Magidow, Maxim Romanov, Avi Shmidman, and Moshe Koppel. 2016. Shamela: A large-scale historical Arabic corpus. In Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities (LT4DH), pages 45–53, Osaka, Japan. find
  17. The COLING 2016 Organizing Committee. find
  18. Yassine Benajiba, Paolo Rosso, and José Miguel BenedíRuiz. 2007. ANERsys: An Arabic Named Entity Recognition System Based on Maximum Entropy. In Computational Linguistics and Intelligent Text Processing, pages 143–153, Berlin, Heidelberg. Springer Berlin Heidelberg. find
  19. Council of Europe Council of Europe. 2001. Common European Framework of Reference for Languages: learning, teaching, assessment. Cambridge University Press. find
  20. Mouath Abu Daoud, Chaimae Abouzahir, Leen Kharouf, Walid Al-Eisawi, Nizar Habash, and Farah E. Shamout. 2025. MedArabiQ: Benchmarking Large Language Models on Arabic Medical Tasks. our card
  21. Kais Dukes, Eric Atwell, and Nizar Habash. 2013. Supervised collaboration for syntactic annotation of Quranic Arabic. Language resources and evaluation, 47(1):33–62. find
  22. Matthias Eck and Chiori Hori. 2005. Overview of the IWSLT 2005 evaluation campaign. In Proceedings of the Second International Workshop on Spoken Language Translation, Pittsburgh, Pennsylvania, USA. find
  23. Omar Einea, Ashraf Elnagar, and Ridhwan Al Debsi. 2019. SANAD: Single-label Arabic News Articles Dataset for automatic text categorization. Data in Brief, 25:104076. find
  24. Mo El-Haj, Sultan Almujaiwel, Damith Premasiri, Tharindu Ranasinghe, and Ruslan Mitkov. 2024. DARES: Dataset for Arabic readability estimation of school materials. In Proceedings of the Workshop on DeTermIt! Evaluating Text Difficulty in a Multilingual Context @ LREC-COLING 2024, pages 103–113, Torino, Italia. ELRA and ICCL. find
  25. Mo El-Haj and Saad Ezzini. 2024. The multilingual corpus of world‘s constitutions (MCWC). In Proceedings of the 6th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT) with Shared Tasks on Arabic LLMs Hallucination and Dialect to MSA Machine Translation @ LREC-COLING 2024, pages 57–66, Torino, Italia. ELRA and ICCL. find
  26. Khalid N. Elmadani, Bashar Alhafni, Hanada Taha, and Nizar Habash. 2025a. BAREC shared task 2025 on Arabic readability assessment. In Proceedings of The Third Arabic Natural Language Processing Conference: Shared Tasks, pages 239–252, Suzhou, China. Association for Computational Linguistics. find
  27. Khalid N. Elmadani, Nizar Habash, and Hanada Taha-Thomure. 2025b. A large and balanced corpus for fine-grained Arabic readability assessment. In Findings of the Association for Computational Linguistics: ACL 2025, pages 16376– 16400, Vienna, Austria. Association for Computational Linguistics. our card
  28. Ahmed Elshabrawy, Muhammed AbuOdeh, Go Inoue, and Nizar Habash. 2023. CamelParser2.0: A state-of-the-art dependency parser for Arabic. In Proceedings of ArabicNLP 2023, pages 170– 180, Singapore (Hybrid). Association for Computational Linguistics. our card
  29. Nizar Habash, Muhammed AbuOdeh, Dima Taji, Reem Faraj, Jamila El Gizuli, and Omar Kallas. 2022. Camel treebank: An open multi-genre Arabic dependency treebank. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 2672–2681, Marseille, France. European Language Resources Association. our card
  30. Nizar Habash, Reem Faraj, and Ryan Roth. 2009. Syntactic Annotation in the Columbia Arabic Treebank. In Proceedings of the International Conference on Arabic Language Resources and Tools, Cairo, Egypt. find
  31. Nizar Habash and David Palfreyman. 2022. ZAE-BUC: An annotated Arabic-English bilingual writer corpus. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 79–88, Marseille, France. European Language Resources Association. find
  32. Nizar Habash and Ryan Roth. 2009. CATiB: The Columbia Arabic treebank. In Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, pages 221–224, Suntec, Singapore. Association for Computational Linguistics. our card
  33. Nizar Habash, Hanada Taha-Thomure, Khalid N. Elmadani, Zeina Zeino, and Abdallah Abushmaes. 2025. Guidelines for fine-grained sentence-level Arabic readability annotation. In Proceedings of the 19th Linguistic Annotation Workshop (LAW-XIX-2025), pages 359–376, Vienna, Austria. Association for Computational Linguistics. find
  34. Go Inoue, Salam Khalifa, and Nizar Habash. 2022. Morphosyntactic tagging with pre-trained language models for Arabic and its dialects. In Findings of the Association for Computational Linguistics: ACL 2022, pages 1708–1719, Dublin, Ireland. Association for Computational Linguistics. find
  35. Christian Khairallah, Salam Khalifa, Reham Marzouk, Mayar Nassar, and Nizar Habash. 2024. Camel morph MSA: A large-scale open-source morphological analyzer for Modern Standard Arabic. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 2683–2691, Torino, Italia. ELRA and ICCL. our card
  36. Fajri Koto, Haonan Li, Sara Shatnawi, Jad Doughman, Abdelrahman Sadallah, Aisha Alraeesi, Khalid Almubarak, Zaid Alyafeai, Neha Sengupta, Shady Shehata, Nizar Habash, Preslav Nakov, and Timothy Baldwin. 2024. ArabicMMLU: Assessing massive multitask language understanding in Arabic. In Findings of the Association for Computational Linguistics: ACL 2024, pages 5622–5640, Bangkok, Thailand. Association for Computational Linguistics. our card
  37. Pierre Lison and Jörg Tiedemann. 2016. Open-Subtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. In Proceedings of the Language Resources and Evaluation Conference (LREC), Portorož, Slovenia. find
  38. Mohamed Maamouri and Ann Bies. 2004. Developing an Arabic Treebank: Methods, Guidelines, Procedures, and Tools. In Proceedings of the Workshop on Computational Approaches to Arabic Script-based Languages (CAASL), pages 2– 9, Geneva, Switzerland. our card
  39. Tarek Naous, Michael J Ryan, Anton Lavrouk, Mohit Chandra, and Wei Xu. 2024. ReadMe++: Benchmarking multilingual language models for multi-domain readability assessment. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 12230–12266, Miami, Florida, USA. Association for Computational Linguistics. find
  40. Joakim Nivre, Daniel Zeman, Filip Ginter, and Francis Tyers. 2017. Universal Dependencies. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts, Valencia, Spain. Association for Computational Linguistics. find
  41. Ossama Obeid, Nasser Zalmout, Salam Khalifa, Dima Taji, Mai Oudah, Bashar Alhafni, Go Inoue, Fadhl Eryani, Alexander Erdmann, and Nizar Habash. 2020a. CAMeL tools: An open source python toolkit for Arabic natural language processing. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 7022–7032, Marseille, France. European Language Resources Association. our card
  42. Ossama Obeid, Nasser Zalmout, Salam Khalifa, Dima Taji, Mai Oudah, Bashar Alhafni, Go Inoue, Fadhl Eryani, Alexander Erdmann, and Nizar Habash. 2020b. CAMeL tools: An open source python toolkit for Arabic natural language processing. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 7022–7032, Marseille, France. our card
  43. Mostafa Saeed and Nizar Habash. 2025. Lemmatization as a classification task: Results from Arabic across multiple genres. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 30014–30029, Suzhou, China. Association for Computational Linguistics. our card
  44. Majdi Sawalha, Faisal Alshargi, Abdallah AlShdaifat, Sane Yagi, and Mohammad A. Qudah. 2019. Construction and annotation of the Jordan comprehensive contemporary Arabic corpus (JCCA). In Proceedings of the Fourth Arabic Natural Language Processing Workshop, pages 148–157, Florence, Italy. Association for Computational Linguistics. find
  45. Eli Smith and Cornelius Van Dyck. 1860. New Testament (Arabic Translation). find
  46. Eli Smith and Cornelius Van Dyck. 1865. Old Testament (Arabic Translation). find
  47. Hanada Taha-Thomure. 2007. Poems and News ( PAJ. k @ð PAª ƒ  @ ). Educational Book House  ( ©K PñJË@ð Qå„ JÊË ø ñK. Q Ë@ H. AJºË@ P@X ) . Hanada Arabic ing Taha-Thomure. find
  48. Language Text 2017. Level- ñJ‚Ó J ’JË é£ @XA Jë Q K AªÓ ( ñ’ JË@ HAK éJ K QªË@ ). Educational Book House .    Q ( ©K Pñ JË@ð Qå „ JÊË ø ñK. Ë@ H. AJºË@ P@X ) . find
  49. Dima Taji, Nizar Habash, and Daniel Zeman. 2017. Universal Dependencies for Arabic. In Proceedings of the Third Arabic Natural Language Processing Workshop, pages 166–176, Valencia, Spain. Association for Computational Linguistics. our card
  50. Toshiyuki Takezawa, Genichiro Kikui, Masahide Mizushima, and Eiichiro Sumita. 2007. Multilingual spoken language corpus development for communication research. In International Journal of Computational Linguistics & Chinese Language Processing, Volume 12, Number 3, September 2007: Special Issue on Invited Papers from ISCSLP 2006, pages 303–324. Ibn Tufail. 1150. Hayy ibn Yaqdhan. Hindawi. Unknown. 12th century. One Thousand and One Nights. find
  51. Waleed A. Yousef, Omar M. Ibrahime, Taha M. Madbouly, and Moustafa A. Mahmoud. 2019. Learning meters of arabic and english poems with recurrent neural networks: a step forward for language understanding and synthesis. arXiv preprint arXiv:1905.05700. ↗
  52. Wajdi Zaghouani. 2014. Critical survey of the freely available arabic corpora. In Workshop on Free/Open-Source Arabic Corpora and Corpora Processing Tools Workshop Programme, page 1. find

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