@inproceedings{albogamy-ramsay-2016-unsupervised,
    title = "Unsupervised Stemmer for {A}rabic Tweets",
    author = "Albogamy, Fahad  and
      Ramsay, Allan",
    editor = "Han, Bo  and
      Ritter, Alan  and
      Derczynski, Leon  and
      Xu, Wei  and
      Baldwin, Tim",
    booktitle = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://preview.aclanthology.org/ingest-emnlp/W16-3912/",
    pages = "78--84",
    abstract = "Stemming is an essential processing step in a wide range of high level text processing applications such as information extraction, machine translation and sentiment analysis. It is used to reduce words to their stems. Many stemming algorithms have been developed for Modern Standard Arabic (MSA). Although Arabic tweets and MSA are closely related and share many characteristics, there are substantial differences between them in lexicon and syntax. In this paper, we introduce a light Arabic stemmer for Arabic tweets. Our results show improvements over the performance of a number of well-known stemmers for Arabic."
}