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# Lemmatization as a Classification Task: Results from Arabic across Multiple Genres

**Authors:** Mostafa Saeed; [Nizar Habash](https://md.mova.elamur.ai/free/en/docs/papers/authors/nizar-habash/) · **Year:** 2025 · **Venue:** Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing **Link:** [aclanthology.org/2025.emnlp-main.1525](https://aclanthology.org/2025.emnlp-main.1525/) · library `ctx-2025-saeed-lemmatization-as-classification-task-results` · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) (ACL Anthology)

[**0** citations](https://openalex.org/works?filter=cites:W4416035516) openalex · 2026-10-18[**2** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/ctx-2025-saeed-lemmatization-as-classification-task-results/) of the 140673 papers whose bibliographies we read[Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing](https://aclanthology.org/volumes/2025.emnlp-main/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/2025.emnlp-main.1525/)
- [PDF](https://aclanthology.org/2025.emnlp-main.1525.pdf)
- [DOI 10.18653/v1/2025.emnlp-main.1525](https://doi.org/10.18653/v1/2025.emnlp-main.1525)
- [arXiv 2506.18399](https://arxiv.org/abs/2506.18399)
- [OpenAlex](https://openalex.org/W4416035516)

**Authors (2)**

1. [Mostafa Saeed](https://openalex.org/A5074666575) h 4 · 37 citations · 16 works
2. [Nizar Habash](https://openalex.org/A5084517393) [ORCID](https://orcid.org/0000-0002-1831-3457) · h 57 · 12233 citations · 359 works

Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart.

## What it is about

Arabic lemmatization is hard because the orthography drops vowels, so one written word has several lemma, POS and gloss readings. The authors frame lemmatization as choosing one Lemma-POS-Gloss (LPG) label from the analyser's candidates, using either a classifier over about 18,000 LPG classes or a clustering of the whole 49K-entry LPG space into about 2,000 semantic clusters. They also build a new multi-genre test set (from the BAREC corpus: poetry, novels, children's stories and others) and align six existing Arabic datasets to one lemma convention. The best setups, clustering and classification with a POS tagger filter, beat the tagger baseline on every test set, with significant gains (McNemar, p < 0.05); the tagger alone cuts the analyser's average of about 15 analyses per word to 1.3, and the LPG level keeps only 2.7 analyses on average before the tagger. A character-level seq2seq model is competitive but hallucinates forms.

## Ideas

- I1. Use the lemma-POS-gloss triple as the unit of the Hebrew dictionary (`macula dict`): store the three together and measure how many candidates per word remain after the POS tagger, the same way the paper's Table 2 does for Arabic.
- I2. A deterministic gloss-overlap rule for ambiguous readings: among candidate senses of a word, prefer the one whose gloss shares content words with the English gloss words already in the frame. It is a table lookup, not the paper's neural cosine similarity.
- I3. Hold out one genre (for example, the late or narrative texts) as a test set for the lemma rules, and report accuracy per genre rather than one overall number.

## For Mova

I1 goes into [The lexicon](https://md.mova.elamur.ai/free/en/docs/languages/hbo/10-lexicon/) and `tools/macula` (`macula dict`). I2 is a rule for the `prag` and `world` senses, tested first on Hebrew glosses. I3 is a test set for the lemma rules in `tools/hbo`; the genre split follows the Hebrew layers in [11. Mishnaic Hebrew (hbo-x-mishnaic)](https://md.mova.elamur.ai/free/en/docs/languages/hbo/11-mishnaic/).

Topics: [Part-of-speech tagging](https://md.mova.elamur.ai/free/en/docs/papers/topics/part-of-speech-tagging/)

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/2025.emnlp-main.1525](https://doi.org/10.18653/v1/2025.emnlp-main.1525) · [arXiv 2506.18399](https://arxiv.org/abs/2506.18399)

```
@inproceedings{saeed2025lemmatization,
  title = {{Lemmatization as a Classification Task: Results from Arabic across Multiple Genres}},
  author = {Saeed, Mostafa and Habash, Nizar},
  booktitle = {Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing},
  year = {2025},
  doi = {10.18653/v1/2025.emnlp-main.1525},
  eprint = {2506.18399},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2025.emnlp-main.1525}
}
```

- [← previous TabletCraft: Bridging a 4,000-Year Cultural Gap with Bidirectional Akkadian NMT and Cuneiform Rendering](https://md.mova.elamur.ai/free/en/docs/papers/coref-2026-wang-tabletcraft-bridging-4-000-year/)
- [next → Nahw: A Comprehensive Benchmark of Arabic Grammar Understanding, Error Detection, Correction, and Explanation](https://md.mova.elamur.ai/free/en/docs/papers/eacl-2026-mubarak-nahw-comprehensive-benchmark-arabic-grammar/)
- [↑ back to Semitic languages](https://md.mova.elamur.ai/free/en/docs/papers/sem/)

## References

46 references, 12 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. Muhamed Al Khalil, Hind Saddiki, Nizar Habash, and Latifa Alfalasi. 2018. A Leveled Reading Corpus of Modern Standard Arabic. In Proceedings of the Language Resources and Evaluation Conference (LREC), Miyazaki, Japan. [find](https://scholar.google.com/scholar?q=Muhamed+Al+Khalil%2C+Hind+Saddiki%2C+Nizar+Habash%2C+and+Latifa+Alfalasi%2E+2018%2E+A+Leveled+Reading+Corpus+of+Modern+Standard+Arabic%2E+In+Proceedings+of+the+Language+Resources+and+Evaluation+Conference+%28LREC%29%2C)
3. Riadh Belkebir and Nizar Habash. 2021. Automatic error type annotation for Arabic. In Proceedings of the 25th Conference on Computational Natural Language Learning, pages 596–606, Online. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Riadh+Belkebir+and+Nizar+Habash%2E+2021%2E+Automatic+error+type+annotation+for+Arabic%2E+In+Proceedings+of+the+25th+Conference+on+Computational+Natural+Language+Learning%2C+pages+596%E2%80%93606%2C+Online%2E+Association+)
4. Nizar Habash and David Palfreyman. 2022. ZAEBUC: 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](https://scholar.google.com/scholar?q=Nizar+Habash+and+David+Palfreyman%2E+2022%2E+ZAEBUC%3A+An+annotated+Arabic%2DEnglish+bilingual+writer+corpus%2E+In+Proceedings+of+the+Thirteenth+Language+Resources+and+Evaluation+Conference%2C+pages+79%E2%80%9388%2C+Marsei)
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12. Nizar Habash, Hanada Taha-Thomure, Khalid N Elmadani, Zeina Zeino, and Abdallah Abushmaes. 2024. Guidelines for fine-grained sentence-level arabic readability annotation. arXiv preprint arXiv:2410.08674. [↗](https://arxiv.org/abs/2410.08674)
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15. 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](https://scholar.google.com/scholar?q=Go+Inoue%2C+Bashar+Alhafni%2C+Nurpeiis+Baimukan%2C+Houda+Bouamor%2C+and+Nizar+Habash%2E+2021%2E+The+interplay+of+variant%2C+size%2C+and+task+type+in+Arabic+pre%2Dtrained+language+models%2E+In+Proceedings+of+the+Sixth+Ara)
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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](https://scholar.google.com/scholar?q=Go+Inoue%2C+Salam+Khalifa%2C+and+Nizar+Habash%2E+2022%2E+Morphosyntactic+tagging+with+pre%2Dtrained+language+models+for+Arabic+and+its+dialects%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+ACL)
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