--- title: "Hamdy Mubarak — papers and cards" description: "Hamdy Mubarak: 8 paper cards in the Research library (2014–2026), with what each paper is about, the ideas Mova took from it, and the co-authors." canonical: "https://research.elamur.ai/free/en/authors/hamdy-mubarak/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/hamdy-mubarak/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Hamdy Mubarak # Hamdy Mubarak 8 paper cards in the Research library · 2014–2026 Hamdy Mubarak wrote 8 of the papers Mova reads, in 2014–2026, most of them on [Treebanks](https://md.research.elamur.ai/free/en/topics/treebanks/); often with [Ahmed Abdelali](https://md.research.elamur.ai/free/en/authors/ahmed-abdelali/), [Kareem Darwish](https://md.research.elamur.ai/free/en/authors/kareem-darwish/), [Mohamed Eldesouki](https://md.research.elamur.ai/free/en/authors/mohamed-eldesouki/). [OpenAlex](https://openalex.org/A5102924560) · [ORCID](https://orcid.org/0000-0002-9051-6240) · [**h-index 30**](https://openalex.org/authors/A5102924560) · [3692 citations](https://openalex.org/authors/A5102924560) · [113 works](https://openalex.org/works?filter=author.id:A5102924560) — openalex-snapshot, 2026-10-14 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.research.elamur.ai/free/en/topics/treebanks/) 1 ## Papers 1. **[Nahw: A Comprehensive Benchmark of Arabic Grammar Understanding, Error Detection, Correction, and Explanation](https://md.research.elamur.ai/free/en/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.research.elamur.ai/free/en/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.research.elamur.ai/free/en/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.research.elamur.ai/free/en/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.research.elamur.ai/free/en/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.research.elamur.ai/free/en/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.research.elamur.ai/free/en/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.research.elamur.ai/free/en/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.research.elamur.ai/free/en/authors/ahmed-abdelali/) ×5 - [Kareem Darwish](https://md.research.elamur.ai/free/en/authors/kareem-darwish/) ×5 - [Mohamed Eldesouki](https://md.research.elamur.ai/free/en/authors/mohamed-eldesouki/) ×2 - [Mohammed Attia](https://md.research.elamur.ai/free/en/authors/mohammed-attia/) ×2 - [Younes Samih](https://md.research.elamur.ai/free/en/authors/younes-samih/) ×2 - Abubakr Mohamed - Ahmed Ali - Kallmeyer Laura - Laura Kallmeyer - Majd Hawasly - [Nadir Durrani](https://md.research.elamur.ai/free/en/authors/nadir-durrani/) - [Shammur Chowdhury](https://md.research.elamur.ai/free/en/authors/shammur-chowdhury/) - Yassine El Kheir ## More papers in the library (17) Pages of the Research library: our summary, the ideas, references and who cites each. - [ArGuard Shared Task: Harmful Content Detection in Arabic Memes and LLM Prompts](https://md.research.elamur.ai/free/en/papers/alam-2026-arguard-shared-task-harmful-content/) — Firoj Alam, Md. Rafiul Biswas, Mohamed Bayan Kmainasi, et al. · 2026 - [Beyond Cultural Knowledge: Evaluating Arabic Cultural Appropriateness of Large Language Models](https://md.research.elamur.ai/free/en/papers/altinisik-2026-cultural-knowledge-evaluating-arabic-cultural/) — Enes Altinisik, Hamdy Mubarak, Masoomali Fatehkia, et al. · 2026 - [WASIL: In-the-Wild Arabic Spoken Interactions with LLMs](https://md.research.elamur.ai/free/en/papers/ali-2026-wasil-wild-arabic-spoken-interactions/) — Zien Sheikh Ali, Hamdy Mubarak, Soon-Gyo Jung, et al. · 2026 - [LAraBench: Benchmarking Arabic AI with Large Language Models](https://md.research.elamur.ai/free/en/papers/abdelali-2024-larabench-benchmarking-arabic-ai-large/) — Ahmed Abdelali, Hamdy Mubarak, Shammur Absar Chowdhury, et al. · 2024 - [QVoice: Arabic Speech Pronunciation Learning Application](https://md.research.elamur.ai/free/en/papers/kheir-2023-qvoice-arabic-speech-pronunciation-learning/) — Yassine El Kheir, Fouad Khnaisser, Shammur Absar Chowdhury, et al. · 2023 - [Unsupervised Data Selection for TTS: Using Arabic Broadcast News as a Case Study](https://md.research.elamur.ai/free/en/papers/baali-2023-unsupervised-data-selection-tts-using/) — Massa Baali, Tomoki Hayashi, Hamdy Mubarak, et al. · 2023 - [ArCovidVac: Analyzing Arabic Tweets About COVID-19 Vaccination](https://md.research.elamur.ai/free/en/papers/mubarak-2022-arcovidvac-analyzing-arabic-tweets-about/) — Hamdy Mubarak, Sabit Hassan, Shammur Absar Chowdhury, et al. · 2022 - [Emojis as Anchors to Detect Arabic Offensive Language and Hate Speech](https://md.research.elamur.ai/free/en/papers/mubarak-2022-emojis-anchors-detect-arabic-offensive/) — Hamdy Mubarak, Sabit Hassan, Shammur Absar Chowdhury · 2022 - [Arabic Offensive Language on Twitter: Analysis and Experiments](https://md.research.elamur.ai/free/en/papers/mubarak-2021-arabic-offensive-language-twitter-analysis/) — Hamdy Mubarak, Ammar Rashed, Kareem Darwish, et al. · 2021 - [Automatic Expansion and Retargeting of Arabic Offensive Language Training](https://md.research.elamur.ai/free/en/papers/mubarak-2021-automatic-expansion-retargeting-arabic-offensive/) — Hamdy Mubarak, Ahmed Abdelali, Kareem Darwish, et al. · 2021 - [Pre-Training BERT on Arabic Tweets: Practical Considerations](https://md.research.elamur.ai/free/en/papers/abdelali-2021-pre-training-bert-arabic-tweets/) — Ahmed Abdelali, Sabit Hassan, Hamdy Mubarak, et al. · 2021 - [A Multi-Platform Arabic News Comment Dataset for Offensive Language Detection](https://md.research.elamur.ai/free/en/papers/chowdhury-2020-multi-platform-arabic-news-comment/) — Shammur Absar Chowdhury, Hamdy Mubarak, Ahmed Abdelali, et al. · 2020 - [Arabic Diacritic Recovery Using a Feature-Rich biLSTM Model](https://md.research.elamur.ai/free/en/papers/darwish-2020-arabic-diacritic-recovery-using-feature/) — Kareem Darwish, Ahmed Abdelali, Hamdy Mubarak, et al. · 2020 - [Arabic Dialect Identification in the Wild](https://md.research.elamur.ai/free/en/papers/abdelali-2020-arabic-dialect-identification-wild/) — Ahmed Abdelali, Hamdy Mubarak, Younes Samih, et al. · 2020 - [Abusive Language Detection on Arabic Social Media](https://md.research.elamur.ai/free/en/papers/mubarak-2017-abusive-language-detection-arabic-social/) — Hamdy Mubarak, Kareem Darwish, Walid Magdy · 2017 - [SemEval-2016 Task 3: Community Question Answering](https://md.research.elamur.ai/free/en/papers/nakov-2016-semeval-2016-task-3-community/) — Preslav Nakov, Lluís Màrquez, Alessandro Moschitti, et al. · 2016 - [The MGB-2 Challenge: Arabic Multi-Dialect Broadcast Media Recognition](https://md.research.elamur.ai/free/en/papers/ali-2016-mgb-2-challenge-arabic-multi/) — Ahmed Ali, Peter Bell, James Glass, et al. · 2016