Deep Neural Network-Based Chinese Semantic Role Labeling

[Abstract] A recent trend in machine learning is to use deep architectures to discover multiple levels of features from data, which has achieved impressive results on various natural language processing (NLP) tasks. We propose a deep neural network⁃based solution to Chinese semantic role labeling (SRL) with its application on message analysis. The solution adopts a six⁃step strategy: text normalization, named entity recognition (NER), Chinese word segmentation and part⁃of⁃speech (POS) tagging, theme classification, SRL, and slot filling. For each step, a novel deep neural network⁃based model is designed and optimized, particularly for smart phone applications. Experiment results on all the NLP sub⁃tasks of the solution show that the proposed neural networks achieve state⁃of⁃the⁃art performance with the minimal computational cost. The speed advantage of deep neural networks makes them more competitive for large⁃scale applications or applications requiring real⁃time response, highlighting the potential of the proposed solution for practical NLP systems.

[Keywords] deep learning; sequence labeling; natural language understanding; convolutional neural network; recurrent neural network