Abstract: Government hotlines undertake a large number of citizens’ demands, which make manual work-order classification time-consuming and laborious. Most of the existing work-order classification methods are based on machine learning or single neural network model. With these methods, it is difficult to effectively understand the context semantic information, and the text feature extraction is not comprehensive. A government hotline work-order classification method fusing RoBERTa and feature extraction is proposed to address the above problems. The proposed method firstly obtains context-aware semantic feature vectors from textual descriptions of work-orders by RoBERTa pre-trained language model. Then, a feature extraction layer based on convolution neural network, bidirectional gated recurrent unit and Self-Attention mechanism is constructed to obtain the local and global features of the work-order semantic encodings, with the process of highlighting the semantic features with great importance for the global features. Finally, the fused feature vectors are input into the classifier to finish work-order classification. Experimental results show the proposed method can achieve better classification performance compared with several baseline methods.

Key words: government hotline, work-order classification, RoBERTa, semantic encoding, feature extraction