@InProceedings{gridach:2016:WSSANLP2016,
  author    = {Gridach, Mourad},
  title     = {Character-Aware Neural Networks for Arabic Named Entity Recognition for Social Media},
  booktitle = {Proceedings of the 6th Workshop on South and Southeast Asian Natural Language Processing (WSSANLP2016)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {23--32},
  abstract  = {Named Entity Recognition (NER) is the task of classifying or labelling atomic
	elements in the text into categories such as Person, Location or Organisation.
	For Arabic language, recognizing named entities is a challenging task because
	of the complexity and the unique characteristics of this language. In addition,
	most of the previous work focuses on Modern Standard Arabic (MSA), however,
	recognizing named entities in social media is becoming more interesting these
	days. Dialectal Arabic (DA) and MSA are both used in social media, which is
	deemed as another challenging task. Most state-of-the-art Arabic NER systems
	count heavily on handcrafted engineering features and lexicons which is time
	consuming. In this paper, we introduce a novel neural network architecture
	which benefits both from character- and word-level representations
	automatically, by using combination of bidirectional LSTM and Conditional
	Random Field (CRF), eliminating the need for most feature engineering.
	Moreover, our model relies on unsupervised word representations learned from
	unannotated corpora. Experimental results demonstrate that our model achieves
	state-of-the-art performance on publicly available benchmark for Arabic NER for
	social media and surpassing the previous system by a large margin.},
  url       = {http://aclweb.org/anthology/W16-3703}
}