@InProceedings{cai-EtAl:2017:Short,
  author    = {Cai, Deng  and  Zhao, Hai  and  Zhang, Zhisong  and  Xin, Yuan  and  Wu, Yongjian  and  Huang, Feiyue},
  title     = {Fast and Accurate Neural Word Segmentation for Chinese},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {608--615},
  abstract  = {Neural models with minimal feature engineering have achieved competitive
	performance against traditional methods for the task of Chinese word
	segmentation. However, both training and working procedures of the current
	neural models are computationally inefficient. In this paper, we propose a
	greedy neural word segmenter with balanced word and character embedding inputs
	to alleviate the existing drawbacks. Our segmenter is truly end-to-end, capable
	of performing segmentation much faster and even more accurate than
	state-of-the-art neural models on Chinese benchmark datasets.},
  url       = {http://aclweb.org/anthology/P17-2096}
}