政策事理图谱构建及应用

Construction and Application of the Policy Event Logic Graph

挖掘政府政策中的事件及其关联,以充分解读政策内涵,将有利于政策的准确宣传与贯彻执行。基于政府政策文本构建事理图谱,设计政策标题感知注意力神经网络并用于政策事件抽取与分类,通过对结果进行数据增强,制定抽取规则实现组成政策事件对的识别与抽取,结合自底向上思想评估政策事件的相对重要性,形成以政策事件为节点、相对重要性为边的政策事理图谱,自动化抽取核心政策事件及关系,为政策宣传和解读提供新的途径。在构建政策事理图谱的实验中,该事件抽取模型结合数据增强方法比最优基线模型在召回率和F-score指标上分别提高了0.79%和0.16%。最后,以营商环境政策为例展示了政策事理图谱的构建及应用过程。

The construction of the event logic graph for policy text can mine policy resource associations and share information,and assist pol⁃icy formulation and publicity.This paper designs the policy title perceptual attention neural network to extract and classify policy events,based on the policy knowledge graph does data augmentation,formulates the extraction rules of the constituent relationship to realize the identi⁃fication and extraction of the constituent policy event pairs,and combines the bottom-up thinking assessment policies relative importance of events,and form the event logic graph of policy text with the policy event as the node and the relative importance as the side.In the process of constructing the event logic graph of policy text experiment and the data augmentation method,the optimal baseline model of the proposed model increased by 0.79%and 0.16%in terms of recall and F-score indicators respectively.Finally,the construction and application proces⁃sof a policy reasoning graph was demonstrated using bussiness environment policies as an example.