@InProceedings{li-EtAl:2017:EMNLP20177,
  author    = {Li, Peng-Hsuan  and  Dong, Ruo-Ping  and  Wang, Yu-Siang  and  Chou, Ju-Chieh  and  Ma, Wei-Yun},
  title     = {Leveraging Linguistic Structures for Named Entity Recognition with Bidirectional Recursive Neural Networks},
  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     = {2664--2669},
  abstract  = {In this paper, we utilize the linguistic structures of texts to improve named
	entity recognition by BRNN-CNN, a special bidirectional recursive network
	attached with a convolutional network. Motivated by the observation that named
	entities are highly related to linguistic constituents, we propose a
	constituent-based BRNN-CNN for named entity recognition. In contrast to
	classical sequential labeling methods, the system first identifies which text
	chunks are possible named entities by whether they are linguistic constituents.
	Then it classifies these chunks with a constituency tree structure by
	recursively propagating syntactic and semantic information to each constituent
	node. This method surpasses current state-of-the-art on OntoNotes 5.0 with
	automatically generated parses.},
  url       = {https://www.aclweb.org/anthology/D17-1282}
}