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

**Authors:** [Christian Khairallah](https://md.mova.elamur.ai/free/en/docs/papers/authors/christian-khairallah/); [Salam Khalifa](https://md.mova.elamur.ai/free/en/docs/papers/authors/salam-khalifa/); [Reham Marzouk](https://md.mova.elamur.ai/free/en/docs/papers/authors/reham-marzouk/); [Mayar Nassar](https://md.mova.elamur.ai/free/en/docs/papers/authors/mayar-nassar/); [Nizar Habash](https://md.mova.elamur.ai/free/en/docs/papers/authors/nizar-habash/) · **Year:** 2024 · **Venue:** LREC-COLING 2024 (Joint International Conference on Computational Linguistics, Language Resources and Evaluation) **Link:** [aclanthology.org/2024.lrec-main.240](https://aclanthology.org/2024.lrec-main.240/) · library `coling-2024-khairallah-camel-morph-msa-large-scale` · **Licence:** [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) — summary only

[**6** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/coling-2024-khairallah-camel-morph-msa-large-scale/) of the 140673 papers whose bibliographies we read[Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)](https://aclanthology.org/volumes/2024.lrec-main/)[licence](https://creativecommons.org/licenses/by-nc/4.0/)

- [Publisher's page](https://aclanthology.org/2024.lrec-main.240/)
- [PDF](https://aclanthology.org/2024.lrec-main.240.pdf)

**Authors (5)**

1. Christian Khairallah
2. Salam Khalifa
3. Reham Marzouk
4. Mayar Nassar
5. Nizar Habash

Citations and author metrics from OpenAlex (); authors with the same name are not always told apart.

## 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

- I1. Category factorization for the Hebrew segmenter: group the prefix and stem categories of `tools/hbo` whose compatible-next sets are identical, so the table lookups shrink. Acceptance test: the output must be identical before and after, run twice.
- I2. Specification scripts for Hebrew: generate the `pre` and `lex` rows from MACULA lemma and morphology data with a script, and keep hand-written conditions only where the script cannot decide.
- I3. Coverage as the headline measure: report the out-of-vocabulary rate of the Hebrew analyzer on a large unseen corpus, split by period, not the size of the lexicon.

## 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](https://md.mova.elamur.ai/free/en/docs/languages/hbo/10-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/}
}
```

- [← previous Arabic Diacritization Using Morphologically Informed Character-Level Model](https://md.mova.elamur.ai/free/en/docs/papers/coling-2024-elmallah-arabic-diacritization-morphologically-informed-character/)
- [next → At the Crossroad of Cuneiform and NLP: Challenges for Fine-grained Part-of-speech Tagging](https://md.mova.elamur.ai/free/en/docs/papers/coling-2024-smidt-crossroad-cuneiform-nlp-challenges-fine/)
- [↑ back to Semitic languages](https://md.mova.elamur.ai/free/en/docs/papers/sem/)

## 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](https://scholar.google.com/scholar?q=Bashar+Alhafni%2C+Nizar+Habash%2C+and+Houda+Bouamor%2E+2022%2E+User%2Dcentric+gender+rewriting%2E+In+Proceedings+of+the+2022+Conference+of+the+North+American+Chapter+of+the+Association+for+Computational+Linguisti)
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](https://scholar.google.com/scholar?q=Bashar+Alhafni%2C+Go+Inoue%2C+Christian+Khairallah%2C+and+Nizar+Habash%2E+2023%2E+Advancements+in+Arabic+grammatical+error+detection+and+correction%3A+An+empirical+investigation%2E+In+Proceedings+of+the+2023+Confer)
4. Ameerah Alothman and Abdul Malik Alsalman. 2020. Arabic morphological analysis techniques: A survey and classification. International Journal of Advanced Computer Science and Applications, pages 214–222. Publisher Copyright: © Science and Information Organization. [find](https://scholar.google.com/scholar?q=Ameerah+Alothman+and+Abdul+Malik+Alsalman%2E+2020%2E+Arabic+morphological+analysis+techniques%3A+A+survey+and+classification%2E+International+Journal+of+Advanced+Computer+Science+and+Applications%2C+pages+214%E2%80%932)
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](https://scholar.google.com/scholar?q=Mohamed+Altantawy%2C+Nizar+Habash%2C+and+Owen+Rambow%2E+2011%2E+Fast+Yet+Rich+Morphological+Analysis%2E+In+Proceedings+of+the+International+Workshop+on+Finite%2DState+Methods+and+Natural+Language+Processing+%28FSMN)
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](https://scholar.google.com/scholar?q=Ramy+Baly%2C+Hazem+Hajj%2C+Nizar+Habash%2C+Khaled+Bashir+Shaban%2C+and+Wassim+El%2DHajj%2E+2017%2E+A+sentiment+treebank+and+morphologically+enriched+recursive+deep+models+for+effective+sentiment+analysis+in+arabic%2E)
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