@InProceedings{mayhew-tsai-roth:2017:EMNLP2017,
  author    = {Mayhew, Stephen  and  Tsai, Chen-Tse  and  Roth, Dan},
  title     = {Cheap Translation for Cross-Lingual Named Entity Recognition},
  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     = {2536--2545},
  abstract  = {Recent work in NLP has attempted to deal with low-resource languages but still
	assumed a resource level that is not present for most languages, e.g., the
	availability of Wikipedia in the target language. We propose a simple method
	for cross-lingual named entity recognition (NER) that works well in settings
	with {\em very} minimal resources. Our approach makes use of a lexicon to
	``translate" annotated data available in one or several high resource
	language(s) into the target language, and learns a standard monolingual NER
	model there. Further, when Wikipedia is available in the target language, our
	method can enhance Wikipedia based methods to yield state-of-the-art NER
	results; we evaluate on 7 diverse languages, improving the state-of-the-art by
	an average of 5.5\% F1 points. With the minimal resources required, this is an
	extremely portable cross-lingual NER approach, as illustrated using a truly
	low-resource language, Uyghur.},
  url       = {https://www.aclweb.org/anthology/D17-1269}
}