The annotated dialectal Arabic corpus (Twt15DA) is collected from Twitter and consists of 311,785 tweets containing 3,858,459 words in total. The unsupervised approach to build the corpus uses an iterative procedure consisting of three main steps: automatic creation of dialectal word lists, selection of seed words, and collection of dialectal sentences. The Pointwise Mutual Information (PMI) association measure, along with the geographical frequency of word occurrence online were used to classify dialectal words. The poor performance of MSA POS tagger on dialectal Arabic contents was exploited in order to extract the dialectal words.

The Twt15DA corpus is available in a manner similar to the TREC Microblog Track (McCreadie et al., 2012), releasing only User ID and Tweet ID pairs along with annotations. The User ID and Tweet ID can be used to crawl Twitter.

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Please cite our paper in any published work using this resource:

@article{althobaiti2021creation,
  title={Creation of annotated country-level dialectal Arabic resources: An unsupervised approach},
  author={Althobaiti, Maha J},
  journal={Natural Language Engineering},
  pages={1--42},
  year={2021},
  publisher={Cambridge University Press}
}