Deep learning in lexical analysis and parsing

Abstract

Lexical analysis and parsing tasks model the deeper properties of the words and their relationships to each other. The commonly used techniques involve word segmentation, part-of-speech tagging and parsing. A typical characteristic of such tasks is that the outputs are structured. Two types of methods are usually used to solve these structured prediction tasks: graph-based methods and transitionbased methods. Graph-based methods differentiate output structures based on their characteristics directly, while transition-based methods transform output construction processes into state transition processes, differentiating sequences of transition actions. Neural network models have been successfully used for both graph-based and transition-based structured prediction. In this chapter, we give a review of applying deep learning in lexical analysis and parsing, and compare with traditional statistical methods.