基于深度强化学习的公共安全领域文本关键词抽取方法
A Keywords Extraction Method for Public Safety Domain Texts Based on Deep Reinforcement Learning
高誉轩 1孙丽娟 2丁洪鑫 2熊子奇2
作者信息
- 1. 成都市河湖保护和智慧水务中心,成都 610072
- 2. 中电科大数据研究院有限公司,贵阳 550022;提升政府治理能力大数据应用技术国家工程研究中心,贵阳 550022
- 折叠
摘要
在国内政务大数据高速发展的背景下,充分利用大量无标注的公共安全领域政策公文文本数据,有效提取文本的关键信息,对提升城市安全治理能力有重要意义.因此,提出一种基于深度强化学习的公共安全领域文本关键词提取模型,通过无监督的方式快速实现文本内容的标签化,以提升用户对公共安全领域文件或事件的检索能力.文章以log-sum范数正则项作为该模型损失函数的稀疏约束,以引导策略网络学习到保留重要词汇、舍弃非重要词汇的策略.同时设计了一种mini-batch大小可变的模型训练方法,通过设置不同的mini-batch大小控制策略网络学习的难度,从而提高策略网络的泛化能力.性能对比结果显示,该模型在测试集的关键词提取任务上优于传统无监督关键词提取方法.
Abstract
With the rapid development of big data in China's government affairs,it is of great signifi-cance to fully utilize a large amount of unlabeled text data in the field of public safety,effectively extract key information from the text,and enhance urban safety governance capabilities.Therefore,a public safety domain text keyword extraction model based on deep reinforcement learning was proposed to quickly label the text content in an unsupervised manner,in order to improve the user's retrieval ability for public safety domain files or events.The paper used the log-sum norm regularization term as the sparse constraint of the loss function of the model to guide the policy network to learn strategies that retain important vocab-ulary and discard unimportant vocabulary.At the same time,a model training method with variable mini-batch sizes was designed,which could control the difficulty of learning the policy network by setting dif-ferent mini batch sizes,thereby improving the generalization capacity of the policy network.The perform-ance comparison results showed that the model outperformed traditional unsupervised methods in the task of keyword extraction.
关键词
深度强化学习/关键词提取/log-sum范数/公共安全大数据Key words
deep reinforcement learning/keyword extraction/log-sum norm/public safety big data引用本文复制引用
基金项目
国家重点研发计划(2023YFC3806001)
出版年
2024
NETL
NSTL
万方数据