Event assigning based on hierarchical features and enhanced association for Chinese mayor's hotline

Author:

Chen Gang1,Cheng Xiaomin12,Chen Jianpeng1,She Xiangrong1,Qin JiaQi1,Chen Jian1

Affiliation:

1. Yangtze River Delta Information Intelligence Innovation Research Institute Wuhu Anhui China

2. School of Information Science and Technology University of Science and Technology of China Hefei Anhui China

Abstract

AbstractNowadays, manual event assignment for Chinese mayor's hotline is still a problem of low efficiency. In this paper, we propose a computer‐aided event assignment method based on hierarchical features and enhanced association. First, hierarchical features of hotline events are extracted to obtain event encoding vectors. Second, the fine‐tuned RoBERTa2RoBERTa model is used to encode the “sanding” responsibility texts of Chinese local departments. Third, an association enhanced attention (AEA) mechanism is proposed to capture the correlation information of the “event‐sanding” splicing vectors for the sake of obtaining matching results of “event‐sanding,” and the matching results are input into the classifier. Finally, the assignment department for is obtained by a department selection module. Experimental results show that our method can achieve better performance compared with several baseline methods on HEAD (a dataset we construct independently). The ablation experiments also demonstrate the validity of each key module in our method.

Publisher

Wiley

Subject

Artificial Intelligence,Computational Mathematics

Reference31 articles.

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3. DevlinJ ChangMW LeeK et al.Bert: Pre‐training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.048052019.

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