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Kareem Darwish

7 paper cards in Mova's library · 2002–2024

Kareem Darwish wrote 7 of the papers Mova reads, in 2002–2024, most of them on Treebanks; often with Ahmed Abdelali, Hamdy Mubarak, Mohamed Eldesouki.

OpenAlex · h-index 38 · 4980 citations · 151 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. Arabic Diacritization Using Morphologically Informed Character-Level Model (2024)
    The paper restores Arabic short vowels with a character-level recurrent model, and adds the morpheme boundaries that a separate segmenter predicts.
  2. 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…
  3. 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.
  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.
  7. Building a Shallow Arabic Morphological Analyser in One Day (2002)
    The paper describes Sebawai, a shallow analyser that returns candidate roots for an Arabic word and is learned from word-root pairs.

Co-authors (16)

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