A structured survey of code-switched Arabic NLP, categorizing the literature, quantifying task and corpus coverage, and listing research gaps and recommendations.
Creating a Large Multi-Layered Representational Repository of Linguistic Code Switched Arabic Data
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abstract
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%.
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cs.CL 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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A Survey of Code-switched Arabic NLP: Progress, Challenges, and Future Directions
A structured survey of code-switched Arabic NLP, categorizing the literature, quantifying task and corpus coverage, and listing research gaps and recommendations.