计算机科学

计算机科学 ›› 2024, Vol. 51 ›› Issue (5): 216-222.doi: 10.11896/jsjkx.230300034

• 人工智能 • 上一篇    下一篇

基于深度多视图网络的政务事件分拨方法

李子琛1, 易修文2,3, 陈顺1,2,3, 张钧波1,2,3, 李天瑞1   

  1. 1 西南交通大学计算机与人工智能学院 成都 611756
    2 北京京东智能城市大数据研究院 北京 100176
    3 京东城市(北京)数字科技有限公司 北京 100176
  • 收稿日期:2023-03-05 修回日期:2023-06-13 出版日期:2024-05-15 发布日期:2024-05-08
  • 通讯作者: 易修文(xiuwenyi@foxmail.com)
  • 作者简介:(zichen_li@126.com)
  • 基金资助:
    国家重点研发计划(2019YFB2103205);北京市科技新星(Z211100002121119)

Government Event Dispatch Approach Based on Deep Multi-view Network

LI Zichen1, YI Xiuwen2,3, CHEN Shun1,2,3, ZHANG Junbo1,2,3, LI Tianrui1   

  1. 1 School of Computing and Artificial Intelligence,Southwest Jiaotong University,Chengdu 611756,China
    2 JD Intelligent Cities Research,Beijing 100176,China
    3 JD Intelligent Cities Technology Co.,Ltd,Beijing 100176,China
  • Received:2023-03-05 Revised:2023-06-13 Online:2024-05-15 Published:2024-05-08
  • About author:LI Zichen,born in 1997,postgraduate.His main research interests include urban computing and deep learning.
    YI Xiuwen,born in 1991, Ph.D, data scientist, researcher, is a member of CCF(No. 45025M). His main research interests include spatio-temporal data mining and deep learning.
  • Supported by:
    National Key R & D Program of China(2019YFB2103205) and Beijing Nova program (Z211100002121119).

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摘要： 12345政务服务便民热线是由各地市政府设立的专门受理热线事项的公共服务平台。随着政府信息化进程的推进,12345热线作为市民与政府交流纽带的重要性大大提高,并对事件处置的效率提出了更高的要求。针对传统事件分拨方法主要依赖于分拨人员人工操作、速度较慢、准确率不高,且需要消耗大量人力资源的问题,文中提出了一种基于深度多视图网络的政务事件分拨方法。首先,通过自监督学习训练带权重的图卷积神经网络,从历史记录中抽取事件归口-分拨部门的分拨行为特征作为事件的归口视图。其次,使用经过政务领域语料微调的BERT模型,提取事件描述与事件标题的语义特征,得到事件的语义视图。然后,使用基于交叉注意力机制的残差网络,将事件的两种视图融合,得到事件的融合表征。最后,将融合表征输入分类器,得到事件分拨的结果。在南通市12345热线的数据集上进行实验,结果表明,所提方法在各项指标上均优于其他基线方法,能够有效提高事件分拨的效率。

关键词: 12345热线, 事件分拨, 文本分类, 多视图学习, 深度学习, 城市计算

Abstract: The 12345 Government Affairs Service Convenience Hotline is a public service platform set up by local governments to handle hotline events.In recent years,with the advancement of government digitization,the significance of the 12345 hotline as a communication link between citizens and government has greatly increased,and there are higher and higher requirements for the efficiency of event handling.Aiming at the problems that the traditional event dispatch method mainly relies on the manual operation of the dispatcher,which is slow in speed,low in accuracy,and consumes a lot of human resources,a government event dispatch method based on deep multi-view network is proposed.Firstly,we train the graph convolutional neural network with weights by self-supervised learning and extract the behavioral representations of event category-dispatched departments from the historical assignment records.After that,the BERT model fine-tuned by the government domain corpus is used to extract the semantic representation of the event description and event title.Then,the residual network based on the attention mechanism is used to fuse multiple views of the event to obtain the fusion representation of the event.Finally,the fusion representation is fed into the classifier to obtain the result of event dispatch.Experiments on the dataset of Nantong 12345 hotline show that the proposed method is superior to other baseline methods in terms of various metrics and can improve the efficiency of event dispatch.

Key words: 12345 hotline, Event dispatch, Text classification, Multi-view learning, Deep learning, Urban computing

中图分类号: 

  • TP399
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