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Compiling and Analysing a Corpus of Transcribed Spoken Gulf Pidgin Arabic Based on Length of Stay in the Gulf

Paper · 2019 · Najah Albaqawi and Michael Oakes

Summary

Gulf Pidgin Arabic (GPA) is a simplified contact variety used between Arabic speakers and migrant workers in the Gulf states, and between the workers themselves. It has had little corpus-based study. The authors recorded and transcribed interviews with 72 expatriate women in Saudi Arabia, producing a corpus of about 72,000 words. The speakers came from six first-language backgrounds. Half had lived in the Gulf for five years or less, and the other half for ten years or more. The authors coded five morpho-syntactic variables: definiteness, conjunctions, copulas, object/possessive pronouns, and agreement in verb and noun-adjective phrases. They then compared how often each group used the Gulf Arabic (GA) variant. Length of residence turned out to matter little. Long-term residents moved significantly toward GA only in definiteness marking and conjunction use. Pidgin-like forms still dominated the other features. The work offers a reusable recipe for building a variationist corpus of an under-described pidgin. It also documents the practical difficulties, from recruiting participants to choosing a transcription convention.

Key points

  • The corpus comes from 72 female informants whose L1s are Malayalam, Punjabi, Bengali, Tagalog, Sunda and Sinhala. The interviews lasted 16–27 minutes and were recorded in Saudi Arabia's Central Province, where Najdi Arabic is spoken.
  • All transcripts use a single unified spelling in Arabic script rather than romanization. Every variant of every variable, including omission, received its own annotation code.
  • Token rates were normalized per number of words, not per minute of speech. The reason is that recent arrivals were expected to talk more slowly.
  • Long-term residents shifted significantly toward GA for definiteness (p=0.002) and conjunctions (p=0.001). They also used bound pronouns more often (49.2% vs 24.2%, p=0.0001), yet free pronouns still prevailed in both groups (about 71–75%).
  • Verb dropping fell from 35.6% to 8.4% (p=0.0002). Fully inflected GA verbs stayed rare (5.2% vs 8.4%, not significant), and noun-adjective agreement rose only slightly (19.3% vs 26.6%, p=0.08).

Interesting ideas

  • I1. Normalizing counts by words produced rather than by speaking time keeps fluency differences from distorting the frequency comparison. Matching the denominator to the actual opportunities for a phenomenon is a general safeguard.
  • I2. An omitted element counts as a real variant with its own code, alongside the overt forms, so zero realizations show up in the counts.
  • I3. A statistically significant rise in one variant can hide that the overall distribution has barely moved, as with bound versus free pronouns. Reporting the whole distribution prevents overclaiming.
  • I4. Each newcomer group is compared with long-term residents who share its L1, which controls for substrate influence and isolates the factor under study.
  • I5. One consistent spelling convention across all transcripts made noisy spoken, non-native language countable and searchable.

For Mova

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

  • I1 (learn): Evaluation reports could express error rates per construction opportunity (e.g., per copula slot or per agreement site), not per sentence, so texts of different density compare fairly.
  • I2 (frames): Frame building could mark dropped verbs, copulas or arguments as explicit null slots, making it inspectable when a role was inferred rather than found in the text.
  • I3 (learn): When a repair rule or training change raises one metric, Mova should also check whether the dominant error class stayed dominant before declaring progress.
  • I4 (learn): Parser or tagger variants could be compared on matched subsets of the same genre or source, serving as a built-in control against confounds from data mix.
  • I5 (tokens): A normalization layer for informal or learner English spellings, applied before tagging and lemmatization, would give downstream stages stable tokens while still logging the original form.

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 2The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 works

🧭 Topics 1Research subjects and languages the work is about.Semitic languages · 3,066 works

Cite

Cite as Najah Albaqawi and Michael Oakes. 2019. Compiling and Analysing a Corpus of Transcribed Spoken Gulf Pidgin Arabic Based on Length of Stay in the Gulf. Proceedings of the 3rd Workshop on Arabic Corpus Linguistics.

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

@inproceedings{albaqawi2019compiling,
  title = {{Compiling and Analysing a Corpus of Transcribed Spoken Gulf Pidgin Arabic Based on Length of Stay in the Gulf}},
  author = {Najah Albaqawi and Michael Oakes},
  booktitle = {Proceedings of the 3rd Workshop on Arabic Corpus Linguistics},
  year = {2019},
  url = {https://aclanthology.org/W19-5602/}
}

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