@InProceedings{yang-zhang-dong:2017:RANLP,
  author    = {Yang, Jie  and  Zhang, Yue  and  Dong, Fei},
  title     = {Neural Reranking for Named Entity Recognition},
  booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna, Bulgaria},
  publisher = {INCOMA Ltd.},
  pages     = {784--792},
  abstract  = {We propose a neural reranking system for named entity recognition (NER),
	leverages recurrent neural network models to learn sentence-level patterns that
	involve named entity mentions. In particular, given an output sentence produced
	by a baseline NER model, we replace all entity mentions, such as \textit{Barack
	Obama}, into their entity types, such as \textit{PER}. The resulting sentence
	patterns contain direct output information, yet is less sparse without specific
	named entities. For example, ``PER was born in LOC'' can be such a pattern.
	LSTM and CNN structures are utilised for learning deep representations of such
	sentences for reranking. Results show that our system can significantly improve
	the NER accuracies over two different baselines, giving the best reported
	results on a standard benchmark.},
  url       = {https://doi.org/10.26615/978-954-452-049-6_101}
}