  Recently, neural network models for natural language processing tasks have
been increasingly focused on for their ability of alleviating the burden of
manual feature engineering. However, the previous neural models cannot extract
the complicated feature compositions as the traditional methods with discrete
features. In this work, we propose a feature-enriched neural model for joint
Chinese word segmentation and part-of-speech tagging 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. Experimental results on five different
datasets show the effectiveness of our proposed model.
