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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, Part-of-speech tagging, Machine translation; often with Hamdy Mubarak, Kareem Darwish, Mohamed Eldesouki.

OpenAlex · ORCID · h-index 25 · 2329 citations · 137 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 · Part-of-speech tagging 1 · Machine translation 1

Papers

  1. 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…
  2. 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.
  3. Challenging Language-Dependent Segmentation for Arabic: An Application to Machine Translation and Part-of-Speech Tagging (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 (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 (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 (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)

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