@InProceedings{wang-xu:2017:I17-1,
  author    = {Wang, Chunqi  and  Xu, Bo},
  title     = {Convolutional Neural Network with Word Embeddings for Chinese Word Segmentation},
  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     = {163--172},
  abstract  = {Character-based sequence labeling framework is flexible and efficient for
	Chinese word segmentation (CWS).
	Recently, many character-based neural models have been applied to CWS. While
	they obtain good performance, they have two obvious weaknesses. The first is
	that they heavily rely on manually designed bigram feature, i.e. they are not
	good at capturing \emph{n}-gram features automatically. The second is that they
	make no use of full word information. For the first weakness, we propose a
	convolutional neural model, which is able to capture rich $n$-gram features
	without any feature engineering.
	For the second one, we propose an effective approach to integrate the proposed
	model with word embeddings.
	We evaluate the model on two benchmark datasets: PKU and MSR. Without any
	feature engineering, the model obtains competitive performance --- 95.7\% on
	PKU and 97.3\% on MSR. Armed with word embeddings, the model achieves
	state-of-the-art performance on both datasets --- 96.5\% on PKU and 98.0\% on
	MSR, without using any external labeled resource.},
  url       = {http://www.aclweb.org/anthology/I17-1017}
}