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# Hamdy Mubarak

8 paper cards in Mova's library · 2014–2026

Hamdy Mubarak wrote 8 of the papers Mova reads, in 2014–2026, most of them on [Treebanks](https://md.mova.elamur.ai/free/en/docs/papers/topics/treebanks/); often with [Ahmed Abdelali](https://md.mova.elamur.ai/free/en/docs/papers/authors/ahmed-abdelali/), [Kareem Darwish](https://md.mova.elamur.ai/free/en/docs/papers/authors/kareem-darwish/), [Mohamed Eldesouki](https://md.mova.elamur.ai/free/en/docs/papers/authors/mohamed-eldesouki/).

[OpenAlex](https://openalex.org/A5102924560) · [ORCID](https://orcid.org/0000-0002-9051-6240) · [**h-index 30**](https://openalex.org/authors/A5102924560) · [3926 citations](https://openalex.org/authors/A5102924560) · [113 works](https://openalex.org/works?filter=author.id:A5102924560) — openalex, 2026-10-18

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Hamdy+Mubarak) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Hamdy+Mubarak) · [dblp](https://dblp.org/search?q=Hamdy+Mubarak) · [ACL Anthology](https://aclanthology.org/search/?q=Hamdy+Mubarak) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Hamdy+Mubarak)

Topics: [Treebanks](https://md.mova.elamur.ai/free/en/docs/papers/topics/treebanks/) 1

## Papers

1. **[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/)** *(2026)* The paper builds Nahw, a benchmark for Modern Standard Arabic grammar taken from school grammar material. It has four tasks in a ladder: grammar understanding (5K multiple-choice questions, Nahw-MCQ, grades 6 to 12), error detection and correction in 100 short passages with about five errors each (Nahw-Passage, 511…
2. **[Beyond Orthography: Automatic Recovery of Short Vowels and Dialectal Sounds in Arabic](https://md.mova.elamur.ai/free/en/docs/papers/acl-2024-kheir-beyond-orthography-automatic-recovery-short/)** *(2024)* Arabic dialects contain sounds that Modern Standard Arabic spelling does not show (such as the g of Gulf and Egyptian loanwords, or p and v), and the written text often lacks short vowels.
3. **[Build Fast and Accurate Lemmatization for Arabic](https://md.mova.elamur.ai/free/en/docs/papers/lingbulk-2018-mubarak-build-fast-accurate-lemmatization-arabic/)** *(2018)* Arabic lemmatization differs from surface stemming: it must restore the citation form, which can require changing stem patterns, not only cutting affixes.
4. **[Arabic Diacritization: Stats, Rules, and Hacks](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2017-darwish-arabic-diacritization-stats-rules-hacks/)** *(2017)* The diacritizer works in two cascaded steps. First it restores the vowels of the word core (the word without its case ending): a word-level bigram model with a Viterbi decoder, backed off to stem dictionaries, stem templates derived from roots (computed with Farasa), a transliteration-mined model for foreign names (a…
5. **[A Neural Architecture for Dialectal Arabic Segmentation](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2017-samih-neural-architecture-dialectal-arabic-segmentation/)** *(2017)* Egyptian Arabic in tweets has no standard spelling and few annotated resources, and the clitics attached to its verbs are missed by MSA tools.
6. **[Arabic Multi-Dialect Segmentation: bi-LSTM-CRF vs. SVM](https://md.mova.elamur.ai/free/en/docs/papers/langstream-2017-eldesouki-arabic-multi-dialect-segmentation-bi/)** *(2017)* The paper segments four Arabic dialects (Egyptian, Levantine, Gulf, Maghrebi) from raw Twitter text. Only about 350 manually segmented tweets per dialect are used for training, with 5-fold cross-validation and a 70/10/20 split. Two approaches are compared. An SVM ranker generates all segmentations of a word from a…
7. **[Farasa: A Fast and Furious Segmenter for Arabic](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2016-abdelali-farasa-fast-furious-segmenter-arabic/)** *(2016)* Farasa splits an Arabic word into prefixes, stem and suffixes. Every character-level split that uses a legal prefix and suffix sequence is generated, and a linear SVM-rank model scores the candidates. The features are stem and affix likelihoods, lexicon membership (a list of valid stems, a gazetteer of names, a list…
8. **[Using Stem-Templates to Improve Arabic POS and Gender/Number Tagging](https://md.mova.elamur.ai/free/en/docs/papers/lingbulk-2014-darwish-stem-templates-improve-arabic-pos/)** *(2014)* The paper builds an end-to-end Arabic pipeline: correction of common spelling confusions (alef forms, ta marbuta and ha, alef maqsura and ya), character-level segmentation with a CRF, POS tagging with a CRF, and gender and number tagging for nouns and adjectives.

## Co-authors (13)

- [Ahmed Abdelali](https://md.mova.elamur.ai/free/en/docs/papers/authors/ahmed-abdelali/) ×5
- [Kareem Darwish](https://md.mova.elamur.ai/free/en/docs/papers/authors/kareem-darwish/) ×5
- [Mohamed Eldesouki](https://md.mova.elamur.ai/free/en/docs/papers/authors/mohamed-eldesouki/) ×2
- [Mohammed Attia](https://md.mova.elamur.ai/free/en/docs/papers/authors/mohammed-attia/) ×2
- [Younes Samih](https://md.mova.elamur.ai/free/en/docs/papers/authors/younes-samih/) ×2
- Abubakr Mohamed
- Ahmed Ali
- Kallmeyer Laura
- Laura Kallmeyer
- Majd Hawasly
- [Nadir Durrani](https://md.mova.elamur.ai/free/en/docs/papers/authors/nadir-durrani/)
- [Shammur Chowdhury](https://md.mova.elamur.ai/free/en/docs/papers/authors/shammur-chowdhury/)
- Yassine El Kheir
- [← previous Hailay Kidu Teklehaymanot](https://md.mova.elamur.ai/free/en/docs/papers/authors/hailay-kidu-teklehaymanot/)
- [next → Hanan Aldarmaki](https://md.mova.elamur.ai/free/en/docs/papers/authors/hanan-aldarmaki/)
- [↑ back to Authors](https://md.mova.elamur.ai/free/en/docs/papers/authors/)
