Lexical analysis and parsing tasks, modeling deeper properties of the words and their relationships to each other, typically involve word segmentation, part-of-speech tagging and parsing. A typical characteristic of such tasks is that the outputs have structured. All of them can fall into three types of structured prediction problems: sequence segmentation, sequence labeling and parsing. In this tutorial, we will introduce two state-of-the-art methods to solve these structured prediction problems: graph-based and transition-based methods. While, traditional graph-based and transition-based methods depend on "feature engineering" work, which costs lots of human labor and may misses many useful features. Deep learning just right can overcome the above "feature engineering" problem. We will further introduction those deep learning models which have been successfully used for both graph-based and transition-based structured prediction.
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