@InProceedings{darwish-EtAl:2017:W17-13,
  author    = {Darwish, Kareem  and  Mubarak, Hamdy  and  Abdelali, Ahmed  and  Eldesouki, Mohamed},
  title     = {Arabic POS Tagging: Don't Abandon Feature Engineering Just Yet},
  booktitle = {Proceedings of the Third Arabic Natural Language Processing Workshop},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {130--137},
  abstract  = {This paper focuses on comparing between using Support Vector Machine based
	ranking (SVM-Rank) and Bidirectional Long-Short-Term-Memory (bi-LSTM)
	neural-network based sequence labeling in building a state-of-the-art Arabic
	part-of-speech tagging system. Using SVM-Rank leads to state-of-the-art
	results, but with a fair amount of feature engineering. Using bi-LSTM,
	particularly when combined with word embeddings, may lead to competitive
	POS-tagging results by automatically deducing latent linguistic features.
	However, we show that augmenting bi-LSTM sequence labeling with some of the
	features that we used for the SVM-Rank based tagger yields to further
	improvements. We also show that gains that realized by using embeddings may not
	be additive with the gains achieved by the features. We are open-sourcing both
	the SVM-Rank and the bi-LSTM based systems for free.},
  url       = {http://www.aclweb.org/anthology/W17-1316}
}