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Building a Corpus of Qatari Arabic Expressions

Paper · 2020 · Sara Al-Mulla and Wajdi Zaghouani

Summary

Arabic NLP resources focus mostly on Modern Standard Arabic. A few dialects, such as Egyptian and Levantine, have some coverage, but Gulf Arabic, and Qatari in particular, has almost none. The authors also argue that traditional Qatari vocabulary is disappearing as English spreads and younger speakers stop using older words. To document and preserve it, they built a pilot lexical corpus of 1000 Qatari traditional expressions, both single words and multiword expressions (MWEs).

They gathered about 600 entries automatically from printed books and web sources, then cleaned them by hand. The remaining 400 came from transcribed interviews with older Qatari citizens. Each entry carries rich metadata, including:

  • a theme
  • a gloss in MSA and an English translation
  • loanword origin
  • POS and inflected forms
  • an example sentence, pronunciation, synonyms and source

An online survey of 50 volunteers checked part of the data. It tested whether people recognised word meanings and could identify where loanwords came from. The corpus is free for research and is intended to support dialectal Arabic text and speech tools.

Key points

  • Size and sources: 1000 entries in total. Roughly 600 came from five printed books and eight online sources, including Mojam, the AlArab newspaper lexicon, the ElBadi message board, AlHewar and the Mufradat lexicon. The other 400 came from interviews.
  • Interviews: eight interviewees, Qatari citizens of both genders aged 40–80, recruited by snowball sampling. They received a topic list but were encouraged to go beyond it and tell the stories behind expressions.
  • Metadata: each entry has 11 fields. Loanwords are tagged with one of four origins: Indian, Turkish, Persian or Western.
  • Survey design: 50 participants. The first section asked for the meanings of 10 expressions shown in example sentences, with an "I don't know" option. The second section asked participants to group 26 expressions.
  • Survey results: correct-answer rates ranged from about 36% to 87.5% across items.
  • Availability: released on data.world under a Creative Commons license. Audio and transcripts are planned for later release.

Interesting ideas

  • I1. Interviewees were given seed topics but invited to tell stories freely, which surfaced far more expressions than direct questioning would.
  • I2. Each lexical entry stores not just the headword but its inflected forms, synonyms, an example sentence and the source it came from.
  • I3. Meaning-recognition questions included an explicit "I don't know" choice, separating ignorance from confident error.
  • I4. Loanwords are tagged with a coarse donor-language class (Indian, Turkish, Persian, Western) rather than a fine etymology.
  • I5. Survey answers were tracked by participant age group, since familiarity with old vocabulary depends on generation.

For Mova

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

  • I1 (learn): when prompting LLM teacher tiers for rare constructions, ask for loose narrative passages on a seed theme rather than lists, to harvest more natural, varied examples.
  • I2 (tokens): MWE and idiom entries in the en lexicon could carry inflected variants plus a provenance field, so the tokenizer can match "kicked the bucket" forms and explain which source licensed the match.
  • I3 (answer): give readers an abstain option in school-test and fable QA evaluation, and score wrong answers apart from abstentions, to measure calibration rather than raw accuracy.
  • I4 (world): a small closed set of origin or register tags on ontology entries could help explain why a word behaves unusually, without needing a full etymological database.
  • I5 (learn): break evaluation results down by text era or genre, so archaic vocabulary in old fables is not hidden inside an aggregate score.

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

 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 Sara Al-Mulla and Wajdi Zaghouani. 2020. Building a Corpus of Qatari Arabic Expressions. Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection.

The paper itself, not this page. BibTeX from the library's record of the work.

@inproceedings{almulla2020building,
  title = {{Building a Corpus of Qatari Arabic Expressions}},
  author = {Sara Al-Mulla and Wajdi Zaghouani},
  booktitle = {Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection},
  year = {2020},
  url = {https://aclanthology.org/2020.osact-1.4/}
}

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