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Identifying Code-switching in Arabizi

Paper · 2022 · Safaa Shehadi and Shuly Wintner

Summary

Arabizi is dialectal Arabic written in Latin letters. It is common on social media, where writers often mix it with English, French, or Arabic script. Standard language identifiers cannot recognise it, so large resources for studying code-switching in Arabizi have been missing. The authors gathered raw text from Arabic-related subreddits. They also collected Twitter timelines of users found through Egyptian and Lebanese seed words. A subset was annotated by hand with word-level language tags. The tag scheme includes a special label for words that could belong to more than one mental lexicon, such as proper names and cognates. Classifiers trained on these annotations tag individual words. Their outputs are then combined into multi-bit sentence tags that record which languages appear in a sentence. Applied to the raw corpus, this pipeline produced about 887K sentences containing Arabizi, of which roughly 446K show code-switching with English or French. The data, annotations and classifiers are public, and they give a basis for corpus-scale research on code-switching and on shared words as possible triggers.

Key points

  • Raw data comes from two sources:
    • Reddit: r/arab, r/arabs, r/egypt, r/jordan, r/lebanon and r/syria, totalling about 3.58M sentences and 59.6M words.
    • Twitter: about 2.47M sentences from 1090 users (936 Egyptian, 154 Lebanese).
  • Spacy, LangDetect, langid and FastText were tested on 100 Arabizi sentences from Tobaili (2016). None of them recognised Arabizi or even Arabic; LangDetect, for example, chose Somali 43 times.
  • Sentence tags derived from word-level predictions reached 0.78 exact accuracy on the 6-bit tag. This clearly beats direct sentence classification. Arabic was identified perfectly in both approaches, thanks to its distinct script.
  • The automatically labelled corpus contains:
    • 886,827 sentences with at least one Arabizi token;
    • 546,883 sentences that are mostly Arabizi;
    • 443,014 sentences with Arabizi–English code-switching;
    • 3,078 sentences with Arabizi–French code-switching.
  • In a manual check of 100 sentences predicted as Arabizi–English code-switched, 77 were correct. Typical errors were informal English spellings ("da", "ya"), Arabic-origin place names, and tokens mixing letters and digits.

Interesting ideas

  • I1. The tag scheme gives words that belong to several lexicons at once (names, cognates) their own "shared" label instead of forcing a single language, which makes the ambiguity explicit and available for later study.
  • I2. Building sentence-level labels from word-level predictions worked much better than training a separate sentence classifier, and every sentence label can be traced back to the tokens behind it.
  • I3. Before building their own tool, the authors ran existing tools on a small sample with known labels and exposed systematic failure modes such as the default to Somali.
  • I4. Data was collected by snowballing: seed words found users, and then all posts by those users were taken. Seeds that collided with other languages or with common names (mazboot, baddi, balki) were dropped after inspection.
  • I5. Character runs were shortened to two letters ("youuuu" becomes "youu"), which reduces sparsity while still showing that a word was stretched for emphasis.

For Mova

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

  • I1 (tokens): The tagger could mark loanwords, foreign names and cognates as shared-origin rather than guessing, so downstream steps can see and explain that uncertainty.
  • I2 (learn): Sentence-level judgments (for example, whether a sentence is in-domain or worth keeping as silver data) could be composed from token-level decisions, so each one stays explainable.
  • I3 (learn): A small gold sample with known answers can act as a negative control, showing when an off-the-shelf witness component falls into a default guess instead of actually working.
  • I4 (learn): Domain text could be gathered the same way, with an explicit audit step that removes seeds which pull in off-target material.
  • I5 (tokens): Informal input could be normalised the same way before lemmatisation, keeping a flag for the elongation so the frames stage could later use the emphasis cue.

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 HaifaHaifa · Israel29 works · 938 citations

 Fields, topics and concepts

📚 Shelves 5The 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 worksHuman-Computer Interaction · 141 works

🧭 Topics 2Research subjects and languages the work is about.Semitic languages · 2,908 worksMultilingual & cross-lingual NLP · 828 works

Cite

Cite as Safaa Shehadi and Shuly Wintner. 2022. Identifying Code-switching in Arabizi. Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2022.wanlp-1.18

@inproceedings{shehadi2022identifying,
  title = {{Identifying Code-switching in Arabizi}},
  author = {Safaa Shehadi and Shuly Wintner},
  booktitle = {Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)},
  year = {2022},
  doi = {10.18653/v1/2022.wanlp-1.18},
  url = {https://doi.org/10.18653/v1/2022.wanlp-1.18}
}

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