@InProceedings{attia-samih-maier:2018:W18-32,
  author    = {Attia, Mohammed  and  Samih, Younes  and  Maier, Wolfgang},
  title     = {GHHT at CALCS 2018: Named Entity Recognition for Dialectal Arabic Using Neural Networks},
  booktitle = {Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  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%.},
  url       = {http://www.aclweb.org/anthology/W18-3212}
}