@InProceedings{samih-EtAl:2017:W17-13,
  author    = {Samih, Younes  and  Attia, Mohammed  and  Eldesouki, Mohamed  and  Abdelali, Ahmed  and  Mubarak, Hamdy  and  Kallmeyer, Laura  and  Darwish, Kareem},
  title     = {A Neural Architecture for Dialectal Arabic Segmentation},
  booktitle = {Proceedings of the Third Arabic Natural Language Processing Workshop},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {46--54},
  abstract  = {The automated processing of Arabic Dialects is challenging due to the lack of
	spelling standards and to the scarcity of annotated data and resources in
	general. Segmentation of words into its constituent parts is an important
	processing building block. In this paper, we show how a segmenter can be
	trained using only 350 annotated tweets using neural networks without any
	normalization or use of lexical features or lexical resources. We deal with
	segmentation as a sequence labeling problem at the character level. We show
	experimentally that our model can rival state-of-the-art methods that rely on
	additional resources.},
  url       = {http://www.aclweb.org/anthology/W17-1306}
}