Joint Learning With BERT-GCN and Multi-Attention for Event Text Classification and Event Assignment

• Published in: IEEE access Vol. 10; pp. 27031 - 27040
• Main Authors: She, Xiangrong, Chen, Jianpeng, Chen, Gang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Ablation; BERT; Classification; Convolution; Convolutional neural networks; Event assignment; event text classification; Feature extraction; Government; government hotline; graph convolution networks; Learning; Modules; multi-attention; Representations; Sanding; Semantics; Social problems; Task analysis; Telephone hotlines; Text categorization
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3156918

Government hotline is closely related to people's lives and plays an important role in solving social problems and maintaining social stability in China. However, the event text of the hotline is inconsistent in length and unclear in elements, so it is a challenge for the operator to manually complete the assignment tasks of hotline event. To address these problems, we propose a joint learning method for event text classification and event assignment for Chinese government hotline. Firstly, graph convolution network (GCN) and BERT are used to process the event text respectively to obtain the corresponding representation vector. Then, the obtained two representation vectors are fused by the dynamic fusion gate to get fusion vector and classified the fusion vector through the text classification. Secondly, we use multi-attention mechanism to process the GCN result vector, BERT result vector and the "sanding" vector to obtain attentive "event-sanding" representation vector and calculate the corresponding department probability distribution. Finally, the historical prior knowledge based reorder model is used to sort the results of the "event-sanding" matching module and output the optimal assignment department of government hotline event. Experimental results show that our method can achieve better performance compared with several baseline approaches. The ablation experiments also demonstrate the validity of each proposed module in our model.