Creating a Large Multi-Layered Representational Repository of Linguistic Code Switched Arabic Data
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Abstract (arXiv)
We present our effort to create a large Multi-Layered representational repository of Linguistic Code-Switched Arabic data. The process involves developing clear annotation standards and Guidelines, streamlining the annotation process, and implementing quality control measures. We used two main protocols for annotation: in-lab gold annotations and crowd sourcing annotations. We developed a web-based annotation tool to facilitate the management of the annotation process. The current version of the repository contains a total of 886,252 tokens that are tagged into one of sixteen code-switching tags. The data exhibits code switching between Modern Standard Arabic and Egyptian Dialectal Arabic representing three data genres: Tweets, commentaries, and discussion fora. The overall Inter-Annotator Agreement is 93.1%.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Sep 2019.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 1; 2025: 1.
4 papers.
- 2022The Decades Progress on Code-Switching Research in NLP: A Systematic Survey on Trends and Challengescited by 35
- 2023Representativeness as a Forgotten Lesson for Multilingual and Code-switched Data Collection and Preparationcited by 16
- 2025A Survey of Code-switched Arabic NLP: Progress, Challenges, and Future Directionscited by 5
- 2024Code-mixed Sentiment and Hate-speech Predictioncited by 4
- 2025A Survey of Code-switched Arabic NLP: Progress, Challenges, and Future Directions23 Jan 2025
- 2024Code-mixed Sentiment and Hate-speech Prediction21 May 2024
- 2023Representativeness as a Forgotten Lesson for Multilingual and Code-switched Data Collection and Preparation31 Oct 2023
- 2022The Decades Progress on Code-Switching Research in NLP: A Systematic Survey on Trends and Challenges19 Dec 2022