An Effective Joint Model for Chinese Word Segmentation and POS Tagging

Chinese word segmentation and Part-of-speech (POS) tagging have been studied for decades. However, most of the previous works mainly focus on pipeline method which will lead to error propagation. In order to make word segmentation and POS tagging jointly in one model, in this paper, we propose an effective neural network model to improve the accuracy of the segmentation and tagging. Our model works based on the hierarchical Long Short-Term Memory (LSTM) and trained jointly in one objective function. What's more, to better utilizing the transition features between tags, we further introduce the transition matrix which can help to search the best tagging sequence. Experiment on Chinese Treebank shows that our model achieves competitive accuracy on word segmentation and POS tagging.