Neural Networks Incorporating Unlabeled and Partially-labeled Data for Cross-domain Chinese Word Segmentation
Neural Networks Incorporating Unlabeled and Partially-labeled Data for Cross-domain Chinese Word Segmentation
Lujun Zhao, Qi Zhang, Peng Wang, Xiaoyu Liu
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 4602-4608.
https://doi.org/10.24963/ijcai.2018/640
Most existing Chinese word segmentation (CWS) methods are usually supervised. Hence, large-scale annotated domain-specific datasets are needed for training. In this paper, we seek to address the problem of CWS for the resource-poor domains that lack annotated data. A novel neural network model is proposed to incorporate unlabeled and partially-labeled data. To make use of unlabeled data, we combine a bidirectional LSTM segmentation model with two character-level language models using a gate mechanism. These language models can capture co-occurrence information. To make use of partially-labeled data, we modify the original cross entropy loss function of RNN. Experimental results demonstrate that the method performs well on CWS tasks in a series of domains.
Keywords:
Natural Language Processing: Phonology, Morphology, and word segmentation
Machine Learning: Neural Networks
Natural Language Processing: Natural Language Processing