@InProceedings{huang-sun-wang:2017:I17-1,
  author    = {Huang, Shen  and  Sun, Xu  and  Wang, Houfeng},
  title     = {Addressing Domain Adaptation for Chinese Word Segmentation with Global Recurrent Structure},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {184--193},
  abstract  = {Boundary features are widely used in traditional Chinese Word Segmentation
	(CWS) methods as they can utilize unlabeled data to help improve the
	Out-of-Vocabulary (OOV) word recognition performance. Although various neural
	network methods for CWS have achieved performance competitive with
	state-of-the-art systems, these methods, constrained by the domain and size of
	the training corpus, do not work well in domain adaptation. In this paper, we
	propose a novel BLSTM-based neural network model which incorporates a global
	recurrent structure designed for modeling boundary features dynamically.
	Experiments show that the proposed structure can effectively boost the
	performance of Chinese Word Segmentation, especially OOV-Recall, which brings
	benefits to domain adaptation. We achieved state-of-the-art results on 6
	domains of CNKI articles, and competitive results to the best reported on the 4
	domains of SIGHAN Bakeoff 2010 data.},
  url       = {http://www.aclweb.org/anthology/I17-1019}
}