大数据认知计算在内容安全管控中的应用

杜雪涛

doi:10.11959/j.issn.2096-0271.2021060

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大数据认知计算在内容安全管控中的应用

专题：大数据支撑的智能应用 | 更新时间：2024-06-03

- 大数据认知计算在内容安全管控中的应用
- Applications of big data cognitive computing in content security governance
- 大数据 2021年7卷第6期页码：53-66
- 作者机构：
- 作者简介：
  
  [ "杜雪涛（1973－），女，中国移动通信集团设计院有限公司网络规划与设计优化研发中心网信安全产品部教授级高级工程师，主要从事网络与信息安全研究工作" ]
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2021060
  中图分类号： TP181
- 网络首发：2021-11，
  
  纸质出版：2021-11-15
- 稿件说明：
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杜雪涛. 大数据认知计算在内容安全管控中的应用[J]. 大数据, 2021,7(6):53-66.

Xuetao DU. Applications of big data cognitive computing in content security governance[J]. Big data research, 2021, 7(6): 53-66.
杜雪涛. 大数据认知计算在内容安全管控中的应用[J]. 大数据, 2021,7(6):53-66. DOI： 10.11959/j.issn.2096-0271.2021060.

Xuetao DU. Applications of big data cognitive computing in content security governance[J]. Big data research, 2021, 7(6): 53-66. DOI： 10.11959/j.issn.2096-0271.2021060.

摘要

通信网络中存在海量垃圾和不良信息，这些信息需要被阅读和理解，以便对其进行有效的特征提取和拦截封堵。基于人工分析的方法已经无法达到目的，需要使用基于大数据的认知计算技术代替人工进行海量的数据分析和理解，帮助人们制订内容安全管控策略。针对电信诈骗治理、不良消息治理、变体消息治理和不良网站治理4个方面遇到的实际问题，分别提出了大数据认知计算的解决方案，并给出了创新性实践的效果。实践表明，提出的解决方案能够快速发现不良信息，有效地提升内容管控质量。

Abstract

In the communication network

there is a mass of bad information needed to be read and understood to extract useful knowledge and features for governance.Methods based on manual analysis can not achieve this goal.It is necessary to adopt the big-data-based cognitive computing technology to help to understand massive data and customize content security strategies.Aiming at four practical problems including telecommunication fraud governance

bad message governance

variant message governance and bad website governance

the big data cognitive computing solutions were put forward

and the practical results were given.The results show that the solutions could find the bad information quickly

and improve the quality of the content security governance effectively.

关键词

Keywords

references

YADAV V , BETHARD S . A survey on recent advances in named entity recognition from deep learning models [J ] . arXiv preprint,2019,arXiv:1910 . 11470v1.

LI J , SUN A X , HAN J L , et al . A survey on deep learning for named entity recognition [J ] . IEEE Transactions on Knowledge and Data Engineering , 2020 :1.

GOYAL A , GUPTA V , KUMAR M . Recent named entity recognition and classification techniques:a systematic review [J ] . Computer Science Review , 2018 , 29 : 21 - 43 .

刘浏 , 王东波 . 命名实体识别研究综述 [J ] . 情报学报 , 2018 , 37 ( 3 ): 329 - 340 .

LIU L , WANG D B . A review on named entity recognition [J ] . Journal of the China Society for Scientific and Technical Information , 2018 , 37 ( 3 ): 329 - 340 .

杨飘 , 董文永 . 基于BERT嵌入的中文命名实体识别方法 [J ] . 计算机工程 , 2020 , 46 ( 4 ): 40 - 45 , 52 .

YANG P , DONG W Y . Chinese named entity recognition method based on BERT embedding [J ] . Computer Engineering , 2020 , 46 ( 4 ): 40 - 45 , 52 .

周法国 , 吴锡坤 , 孙泰 , 等 . 基于转移学习的中文命名实体识别 [J ] . 计算机工程与应用 , 2018 , 54 ( 5 ): 117 - 121 .

ZHOU F G , WU X K , SUN T , et al . Chinese named entity recognition based on transformation learning [J ] . Computer Engineering and Applications , 2018 , 54 ( 5 ): 117 - 121 .

李明扬 , 孔芳 . 融入自注意力机制的社交媒体命名实体识别 [J ] . 清华大学学报(自然科学版) , 2019 , 59 ( 6 ): 461 - 467 .

LI M Y , KONG F . Combined self-attention mechanism for named entity recognition in social media [J ] . Journal of Tsinghua University (Science and Technology) , 2019 , 59 ( 6 ): 461 - 467 .

VASWANI A , SHAZEER N , PARMAR N , et al . Attention is all you need [C ] // Proceedings of the 31st International Conference on Neural Information Processing Systems . New York:ACM Press , 2017 : 6000 - 6010 .

GE L H , MOH T S . Improving text classification with word embedding [C ] // Proceedings of 2017 IEEE International Conference on Big Data . Piscataway:IEEE Press , 2017 : 1796 - 1805 .

LIU Q , HUANG H Y , GAO Y , et al . Task-oriented word embedding for text classification [C ] // Proceedings of the 27th International Conference on Computational Linguistics . Stroudsburg:Association for Computational Linguistics , 2018 : 2023 - 2032 .

SHAHEEN Z , WOHLGENANNT G , FILTZ E . Large scale legal text classification using transformer models [J ] . arXiv preprint,2020,arXiv:2010.12871 .

王玲 . 基于Word2Vec词嵌入和双向长短时记忆网络的文本分类实现 [J ] . 电子技术与软件工程 , 2020 ( 15 ): 70 - 71 .

WANG L . Implementation of text classification based on Word2Vec word embedding and bidirectional long-term and short-term memory network [J ] . Electronic Technology ＆ Software Engineering , 2020 ( 15 ): 70 - 71 .

DU J C , GUI L , XU R F , et al . A convolutional attention model for text classification [M ] // Natural Language Processing and Chinese Computing . Cham : Springer , 2018 : 183 - 195 .

GAO S , RAMANATHAN A , TOURASSI G . Hierarchical convolutional attention networks for text classification [C ] // Proceedings of the 3rd Workshop on Representation Learning for NLP . Stroudsburg:Association for Computational Linguistics , 2018 : 11 - 23 .

LIU G , GUO J B . Bidirectional LSTM with attention mechanism and convolutional layer for text classification [J ] . Neurocomputing , 2019 , 337 : 325 - 338 .

ZHENG J , ZHENG L M . A hybrid bidirectional recurrent convolutional neural network attention-based model for text classification [J ] . IEEE Access , 2019 , 7 : 106673 - 106685 .

闫跃 , 霍其润 , 李天昊 , 等 . 融合多重注意力机制的卷积神经网络文本分类设计与实现 [J ] . 小型微型计算机系统 , 2021 , 42 ( 2 ): 362 - 367 .

YAN Y , HUO Q R , LI T H , et al . Design and implementation of text classification based on convolutional neural network with multiple attention mechanisms [J ] . Journal of Chinese Computer Systems , 2021 , 42 ( 2 ): 362 - 367 .

许晓泓 , 何霆 , 王华珍 , 等 . 结合Transformer模型与深度神经网络的数据到文本生成方法 [J ] . 重庆大学学报 , 2020 , 43 ( 7 ): 91 - 100 .

XU X H , HE T , WANG H Z , et al . Research on data-to-text generation based on transformer model and deep neural network [J ] . Journal of Chongqing University , 2020 , 43 ( 7 ): 91 - 100 .

PAWADE D , SAKHAPARA A , JAIN M , et al . Story scrambler - automatic text generation using word level RNNLSTM [J ] . International Journal of Information Technology and Computer Science , 2018 , 10 ( 6 ): 44 - 53 .

钱揖丽 , 马雪雯 . 基于句子级LSTM编码的文本标题生成 [J ] . 计算机应用与软件 , 2021 , 38 ( 5 ): 190 - 195 .

QIAN Y L , MA X W . Text headline generation based on sentence-level LSTM encoding [J ] . Computer Applications and Software , 2021 , 38 ( 5 ): 190 - 195 .

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