Deep Neural Network-Based Chinese Semantic Role Labeling

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.