@InProceedings{sato-EtAl:2017:I17-2,
  author    = {Sato, Motoki  and  Shindo, Hiroyuki  and  Yamada, Ikuya  and  Matsumoto, Yuji},
  title     = {Segment-Level Neural Conditional Random Fields for Named Entity Recognition},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {97--102},
  abstract  = {We present Segment-level Neural CRF, 
	which combines neural networks with a linear chain CRF
	for segment-level sequence modeling tasks 
	such as named entity recognition (NER) and syntactic chunking.
	Our segment-level CRF can consider higher-order label dependencies compared
	with conventional word-level CRF. 
	Since it is difficult to consider all possible variable length segments, 
	our method uses segment lattice constructed from the word-level tagging model
	to reduce the search space.
	Performing experiments on NER and chunking, we demonstrate that our method
	outperforms conventional word-level CRF with neural networks.},
  url       = {http://www.aclweb.org/anthology/I17-2017}
}