@inproceedings{attia-etal-2018-ghht,
    title = "{GHHT} at {CALCS} 2018: Named Entity Recognition for Dialectal {A}rabic Using Neural Networks",
    author = "Attia, Mohammed  and
      Samih, Younes  and
      Maier, Wolfgang",
    editor = "Aguilar, Gustavo  and
      AlGhamdi, Fahad  and
      Soto, Victor  and
      Solorio, Thamar  and
      Diab, Mona  and
      Hirschberg, Julia",
    booktitle = "Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/W18-3212/",
    doi = "10.18653/v1/W18-3212",
    pages = "98--102",
    abstract = "This paper describes our system submission to the CALCS 2018 shared task on named entity recognition on code-switched data for the language variant pair of Modern Standard Arabic and Egyptian dialectal Arabic. We build a a Deep Neural Network that combines word and character-based representations in convolutional and recurrent networks with a CRF layer. The model is augmented with stacked layers of enriched information such pre-trained embeddings, Brown clusters and named entity gazetteers. Our system is ranked second among those participating in the shared task achieving an FB1 average of 70.09{\%}."
}