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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; often with Ahmed Abdelali, Kareem Darwish, Mohamed Eldesouki.

OpenAlex · ORCID · h-index 30 · 3926 citations · 113 works — 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 · Semantic Scholar · dblp · ACL Anthology · ORCID

Topics: Treebanks 1

Papers

  1. Nahw: A Comprehensive Benchmark of Arabic Grammar Understanding, Error Detection, Correction, and Explanation (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 (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 (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 (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 (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 (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 (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 (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)

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