@InProceedings{poostchi-EtAl:2016:COLING,
  author    = {Poostchi, Hanieh  and  Zare Borzeshi, Ehsan  and  Abdous, Mohammad  and  Piccardi, Massimo},
  title     = {PersoNER: Persian Named-Entity Recognition},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {3381--3389},
  abstract  = {Named-Entity Recognition (NER) is still a challenging task for languages with
	low digital resources. The main difficulties arise from the scarcity of
	annotated corpora and the consequent problematic training of an effective NER
	pipeline. To abridge this gap, in this paper we target the Persian language
	that is spoken by a population of over a hundred million people world-wide. We
	first present and provide ArmanPerosNERCorpus, the first manually-annotated
	Persian NER corpus. Then, we introduce PersoNER, an NER pipeline for Persian
	that leverages a word embedding and a sequential max-margin classifier. The
	experimental results show that the proposed approach is capable of achieving
	interesting MUC7 and CoNNL scores while outperforming two alternatives based on
	a CRF and a recurrent neural network.},
  url       = {http://aclweb.org/anthology/C16-1319}
}