Long Short-Term Memory Neural Networks for Chinese Word Segmentation

Currently most of state-of-the-art meth-ods for Chinese word segmentation are based on supervised learning, whose fea-tures aremostly extracted from a local con-text. 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) neu-ral network to keep the previous impor-tant information inmemory cell and avoids the limit of window size of local context. Experiments on PKU, MSRA and CTB6 benchmark datasets show that our model outperforms the previous neural network models and state-of-the-art methods.