Long Short-Term Memory Neural Networks for Chinese Word Segmentation

@inproceedings{Chen2015LongSM,
  title={Long Short-Term Memory Neural Networks for Chinese Word Segmentation},
  author={Xinchi Chen and Xipeng Qiu and Chenxi Zhu and Pengfei Liu and Xuanjing Huang},
  booktitle={EMNLP},
  year={2015}
}
Currently most of state-of-the-art methods for Chinese word segmentation are based on supervised learning, whose features aremostly extracted from a local context. Thesemethods cannot utilize the long distance information which is also crucial for word segmentation. In this paper, we propose a novel neural network model for Chinese word segmentation, which adopts the long short-term memory (LSTM) neural network to keep the previous important information inmemory cell and avoids the limit of… CONTINUE READING
Highly Influential
This paper has highly influenced 26 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 115 citations. REVIEW CITATIONS

11 Figures & Tables

Topics

Statistics

020402015201620172018
Citations per Year

116 Citations

Semantic Scholar estimates that this publication has 116 citations based on the available data.

See our FAQ for additional information.