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Beyond Cairo: Sa'idi Egyptian Arabic Literary Corpus Construction and Analysis

Paper · 2025 · Mai Mohamed Eida and Nizar Habash

Summary

Egyptian Arabic NLP has been built almost entirely around the Cairo variety. Sa'idi Egyptian Arabic (SEA), spoken by tens of millions in Upper Egypt, has no released text resources. Its speakers tend to suppress stigmatized dialect markers when writing online, so tweets are a poor source. The authors turn to literature, where writers use the dialect deliberately. They built an open SEA corpus of about 4.5 million words:

  • Novels from a self-publishing web forum's "Sa'idi" genre, with character dialogue isolated using punctuation markers.
  • OCR-digitized and hand-corrected poetry by two Sa'idi poets.

A manual check of sampled text against two older dialectal surveys confirmed that marked SEA phonological and morphological features occur in the corpus. The authors also normalized a portion of the corpus with the CODA* orthography conventions. They then tested how well an existing Egyptian morphological pipeline handles SEA, reaching 81% overall accuracy. The resource extends CODA* to SEA and gives a baseline for building future SEA analyzers.

Key points

  • Novels: 58 novels by 26 female authors aged 17–40, totalling 4,541,835 words. Roughly 1.42M of these words come from dialogue. Only 4 authors report living in Sa'idi cities.
  • Poetry: 355 poems (27,170 words) by Hisham Algakh and Abdel Rahman el-Abnudi, digitized by OCR and corrected by hand.
  • Feature validation: a randomized 16,000-word sample was checked against the surveys of Behnstedt and Woidich (1985) and Khalafallah (1969).
  • CODA* normalization: 15,000 words were annotated, and 74.7% of them needed no change. Comparable figures are 86.54% for Palestinian and 78.1% for Emirati Arabic.
  • Morphological analysis: 4,000 sentences were processed with the CALIMA analyzer plus the BERT-Disambiguator via CamelTools.
    • Overall accuracy was 81%, and 16% of words were OOV, with glosses as the main error source.
    • The SEA-specific future clitic received no analysis at all.
    • The release on GitHub (SaidiCorpus2025) contains partial texts with sentences shuffled to respect copyright.

Interesting ideas

  • I1. Corpus representativeness is checked by looking for diagnostic features taken from independent reference descriptions, rather than trusting the source label; this is a cheap audit of whether data really reflects the target variety.
  • I2. Dialogue is separated from narration using each author's consistent punctuation conventions, and documents lacking a clear boundary are dropped instead of guessed at.
  • I3. Text is normalized into a conventional orthography before analysis, while the rate of modified words is reported as a measure of how far the input is from the standard.
  • I4. Analyzer errors are split into true OOV cases versus cases where the right reading existed but the disambiguator ranked another first. This separates lexicon gaps from context-model failures.
  • I5. A tool built for a closely related variety is reused to pre-annotate new data, with humans correcting it, turning a missing resource into a semi-automatic annotation task.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (learn): build checklists of diagnostic constructions per genre (e.g., fables, word problems) and verify that silver training sets actually contain them before trusting coverage.
  • I2 (frames): a per-document detector of quote/dialogue markers could assign utterances to speakers, giving cleaner input for speech-act and who-said-what frames.
  • I3 (tokens): add an explicit, logged spelling-normalization layer for informal English and report the share of tokens changed as an input-difficulty signal.
  • I4 (tokens): keep the full candidate list from the morphology/tagger and log "correct but not chosen" separately, so fixes target the lexicon or the ranker appropriately.
  • I5 (learn): bootstrap annotations for new English registers (old fables, dialect-heavy dialogue) from the existing parser, then measure and repair systematic misses as repair rules.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of Illinois Urbana-ChampaignUrbana · United States184 works · 69,372 citations🎓New York University Abu DhabiAbu Dhabi · United Arab Emirates122 works · 4,602 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 1Research subjects and languages the work is about.Semitic languages · 2,908 works

Cite

Cite as Mai Mohamed Eida and Nizar Habash. 2025. Beyond Cairo: Sa'idi Egyptian Arabic Literary Corpus Construction and Analysis. Proceedings of the 5th International Conference on Natural Language Processing for Digital Humanities.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2025.nlp4dh-1.26

@inproceedings{eida2025beyond,
  title = {{Beyond Cairo: Sa'idi Egyptian Arabic Literary Corpus Construction and Analysis}},
  author = {Mai Mohamed Eida and Nizar Habash},
  booktitle = {Proceedings of the 5th International Conference on Natural Language Processing for Digital Humanities},
  year = {2025},
  doi = {10.18653/v1/2025.nlp4dh-1.26},
  url = {https://doi.org/10.18653/v1/2025.nlp4dh-1.26}
}

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