[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/authors/ahmed-abdelali/) [home](https://md.mova.elamur.ai/free/en/) / [docs](https://md.mova.elamur.ai/free/en/docs/) / [papers](https://md.mova.elamur.ai/free/en/docs/papers/) / [authors](https://md.mova.elamur.ai/free/en/docs/papers/authors/) / Ahmed Abdelali # Ahmed Abdelali 6 paper cards in Mova's library · 2014–2017 Ahmed Abdelali wrote 6 of the papers Mova reads, in 2014–2017, most of them on [Treebanks](https://md.mova.elamur.ai/free/en/docs/papers/topics/treebanks/), [Part-of-speech tagging](https://md.mova.elamur.ai/free/en/docs/papers/topics/part-of-speech-tagging/), [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/); often with [Hamdy Mubarak](https://md.mova.elamur.ai/free/en/docs/papers/authors/hamdy-mubarak/), [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/A5047182137) · [ORCID](https://orcid.org/0000-0002-4160-8181) · [**h-index 25**](https://openalex.org/authors/A5047182137) · [2329 citations](https://openalex.org/authors/A5047182137) · [137 works](https://openalex.org/works?filter=author.id:A5047182137) — 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=Ahmed+Abdelali) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Ahmed+Abdelali) · [dblp](https://dblp.org/search?q=Ahmed+Abdelali) · [ACL Anthology](https://aclanthology.org/search/?q=Ahmed+Abdelali) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Ahmed+Abdelali) Topics: [Treebanks](https://md.mova.elamur.ai/free/en/docs/papers/topics/treebanks/) 1 · [Part-of-speech tagging](https://md.mova.elamur.ai/free/en/docs/papers/topics/part-of-speech-tagging/) 1 · [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/) 1 ## Papers 1. **[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… 2. **[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. 3. **[Challenging Language-Dependent Segmentation for Arabic: An Application to Machine Translation and Part-of-Speech Tagging](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2017-sajjad-challenging-language-dependent-segmentation-arabic/)** *(2017)* The paper asks whether Arabic can be segmented without a language-specific morphological tool such as MADAMIRA or Farasa. 4. **[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… 5. **[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… 6. **[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 (12) - [Hamdy Mubarak](https://md.mova.elamur.ai/free/en/docs/papers/authors/hamdy-mubarak/) ×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 - [Nadir Durrani](https://md.mova.elamur.ai/free/en/docs/papers/authors/nadir-durrani/) ×2 - [Younes Samih](https://md.mova.elamur.ai/free/en/docs/papers/authors/younes-samih/) ×2 - Fahim Dalvi - Hassan Sajjad - Kallmeyer Laura - Laura Kallmeyer - [Stephan Vogel](https://md.mova.elamur.ai/free/en/docs/papers/authors/stephan-vogel/) - [Yonatan Belinkov](https://md.mova.elamur.ai/free/en/docs/papers/authors/yonatan-belinkov/) - [← previous Ahmad Aljanaideh](https://md.mova.elamur.ai/free/en/docs/papers/authors/ahmad-aljanaideh/) - [next → Akihiko Kato](https://md.mova.elamur.ai/free/en/docs/papers/authors/akihiko-kato/) - [↑ back to Authors](https://md.mova.elamur.ai/free/en/docs/papers/authors/)