Recently, many neural network models have been applied to Chinese word segmentation. However, such models focus more on collecting local information while long distance dependencies are not well learned. To integrate local features with long distance dependencies, we propose a dependency-based gated recursive neural network. Local features are first collected by bi-directional long short term memory network, then combined and refined to long distance dependencies via gated recursive neural network. Experimental results show that our model is a competitive model for Chinese word segmentation.
Xu and Sun REF collects local features by Bi-LSTM network and combines them to long distance dependencies via GRNN.
Dependency-based Gated Recursive Neural Network for Chinese Word Segmentation
ACL
Computer Science