  Recently, it is quite common to integrate Chinese sequence labeling results
to enhance syntactic and semantic parsing. However, little attention has been
paid to the utility of hierarchy and structure information encoded in syntactic
and semantic features for Chinese sequence labeling tasks. In this paper, we
propose a novel framework to encode syntactic structure features and semantic
information for Chinese sequence labeling tasks with graph convolutional
networks (GCN). Experiments on five benchmark datasets, including Chinese word
segmentation and part-of-speech tagging, demonstrate that our model can
effectively improve the performance of Chinese labeling tasks.
