Exploring Representations from Unlabeled Data with Co-training for Chinese Word Segmentation

Nowadays supervised sequence labeling models can reach competitive performance on the task of Chinese word segmenta-tion. However, the ability of these mod-els is restricted by the availability of an-notated data and the design of features. We propose a scalable semi-supervised fea-ture engineering approach. In contrast to previous works using pre-defined task-specific features with fixed values, we dy-namically extract representations of label distributions from both an in-domain cor-pus and an out-of-domain corpus. We update the representation values with a semi-supervised approach. Experiments on the benchmark datasets show that our approach achieve good results and reach an f-score of 0.961. The feature engineer-ing approach proposed here is a general iterative semi-supervised method and not limited to the word segmentation task.