@InProceedings{zalmout-habash:2017:EMNLP2017,
  author    = {Zalmout, Nasser  and  Habash, Nizar},
  title     = {Don't Throw Those Morphological Analyzers Away Just Yet: Neural Morphological Disambiguation for Arabic},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {704--713},
  abstract  = {This paper presents a model for Arabic morphological disambiguation based on
	Recurrent Neural Networks (RNN). We train Long Short-Term Memory (LSTM) cells
	in several configurations and embedding levels to model the various
	morphological features. Our experiments show that these models outperform
	state-of-the-art systems without explicit use of feature engineering. However,
	adding learning features from a morphological analyzer to model the space of
	possible analyses provides additional improvement.
	We make use of the resulting morphological models for scoring and ranking the
	analyses of the morphological analyzer for morphological disambiguation. The
	results show significant gains in accuracy across several evaluation metrics.
	Our system results in 4.4% absolute increase over the state-of-the-art in full
	morphological analysis accuracy (30.6% relative error reduction), and 10.6% 
	(31.5% relative error reduction) for out-of-vocabulary words.},
  url       = {https://www.aclweb.org/anthology/D17-1073}
}