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Camel Morph MSA: A Large-Scale Open-Source Morphological Analyzer for Modern Standard Arabic

What it is about

The paper presents an open-source analyzer and generator for Modern Standard Arabic, built in the CAMeL Morph framework. The lexicon has over 100K lemmas, with rarely modelled features of classical origin. The framework compiles hand-written specifications into a six-table database in the style of the Buckwalter analyzer. The authors add two extensions: category factorization, which merges compiled categories with identical dependencies to speed up analysis, and specification scripts, which generate parts of the specification automatically before the database is built. The database gives about 1.45 billion analyses and about 535 million unique diacritizations. Its out-of-vocabulary rate is about 36% lower than that of SAMA on a 10-billion-word corpus. It works with the Camel Tools package.

Ideas

For Mova

I1 and I2 go into tools/hbo (the segmenter tables pre and lex) and into tools/macula as the row generator for the Torah dictionary. I3 is the measure for tools/hbo, reported with The lexicon.

Cite

The paper itself, not this card. BibTeX from the library's record of the work.

@inproceedings{khairallah2024camel,
  title = {{Camel Morph MSA: A Large-Scale Open-Source Morphological Analyzer for Modern Standard Arabic}},
  author = {Khairallah, Christian and Khalifa, Salam and Marzouk, Reham and Nassar, Mayar and Habash, Nizar},
  booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
  year = {2024},
  url = {https://aclanthology.org/2024.lrec-main.240/}
}

References

32 references, 7 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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  2. Bashar Alhafni, Nizar Habash, and Houda Bouamor. 2022. User-centric gender rewriting. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 618–631, Seattle, United States. Association for Computational Linguistics. find
  3. Bashar Alhafni, Go Inoue, Christian Khairallah, and Nizar Habash. 2023. Advancements in Arabic grammatical error detection and correction: An empirical investigation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 6430–6448, Singapore. Association for Computational Linguistics. find
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  5. Mohamed Altantawy, Nizar Habash, and Owen Rambow. 2011. Fast Yet Rich Morphological Analysis. In Proceedings of the International Workshop on Finite-State Methods and Natural Language Processing (FSMNLP), Blois, France. find
  6. Ramy Baly, Hazem Hajj, Nizar Habash, Khaled Bashir Shaban, and Wassim El-Hajj. 2017. A sentiment treebank and morphologically enriched recursive deep models for effective sentiment analysis in arabic. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), 16(4):23. find
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  11. Nizar Habash, Salam Khalifa, Fadhl Eryani, Owen Rambow, Dana Abdulrahim, Alexander Erdmann, Reem Faraj, Wajdi Zaghouani, Houda Bouamor, Nasser Zalmout, Sara Hassan, Faisal Al shargi, Sakhar Alkhereyf, Basma Abdulkareem, Ramy Eskander, Mohammad Salameh, and Hind Saddiki. 2018. Unified Guidelines and Resources for Arabic Dialect Orthography. In Proceedings of the International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. find
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  13. Nizar Habash and Owen Rambow. 2006. MAGEAD: A morphological analyzer and generator for the Arabic dialects. In Proceedings of the International Conference on Computational Linguistics and the Conference of the Association for Computational Linguistics (COLING-ACL), pages 681–688, Sydney, Australia. find our card
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  16. Go Inoue, Bashar Alhafni, Nurpeiis Baimukan, Houda Bouamor, and Nizar Habash. 2021. The interplay of variant, size, and task type in Arabic pre-trained language models. In Proceedings of the Sixth Arabic Natural Language Processing Workshop, pages 92–104, Kyiv, Ukraine (Virtual). Association for Computational Linguistics. find
  17. 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
  18. Christian Khairallah, Reham Marzouk, Salam Khalifa, Mayar Nassar, and Nizar Habash. 2024. Computational morphology and lexicography modeling of Modern Standard Arabic nominals. In Findings of the Association for Computational Linguistics: EACL 2024, pages 1071–1084, St. Julian’s, Malta. Association for Computational Linguistics. find our card
  19. George Kiraz. 1994. Multi-tape Two-level Morphology: A Case study in Semitic Non-Linear Morphology. In Proceedings of the International Conference on Computational Linguistics (COLING), pages 180–186, Kyoto, Japan. find our card
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  27. Nasser Zalmout and Nizar Habash. 2017. Don’t throw those morphological analyzers away just yet: Neural morphological disambiguation for Arabic. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 704–713, Copenhagen, Denmark. 8. find our card
  28. Language Resource References Tim Buckwalter. 2004. Buckwalter Arabic Morphological Analyzer Version 2.0. LDC. Linguistic Data Consortium, Catalog No.: LDC2004L02, ISLRN 694-194-540-336-4. find
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