A Long Dependency Aware Deep Architecture for Joint Chinese Word Segmentation and POS Tagging

Long-term context is crucial to joint Chinese word segmentation and POS tagging (S&T) task. However, most of machine learning based methods extract features from a window of characters. Due to the limitation of window size, these methods can not exploit the long distance information. In this work, we propose a long dependency aware deep architecture for joint S&T task. Specifically, to simulate the feature templates of traditional discrete feature based models, we use different filters to model the complex compositional features with convolutional and pooling layer, and then utilize long distance dependency information with recurrent layer. Experiment results on five different datasets show the effectiveness of our proposed model.