计算机科学 ›› 2015, Vol. 42 ›› Issue (1): 272-275.doi: 10.11896/j.issn.1002-137X.2015.01.060

• 人工智能 • 上一篇    下一篇

网络敏感信息自适应多重过滤模型研究

胡传志,程显毅,曹小峰   

  1. 南通大学计算机科学与技术学院 南通226019;华东师范大学计算机科学技术系 上海200241,南通大学计算机科学与技术学院 南通226019,南通大学计算机科学与技术学院 南通226019
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受国家自然科学基金(61340037),南通大学自然科学基金(13Z002)资助

Reserch on Model of Multi Filtering for Adaption about Network Sensitive Information

HU Chuan-zhi, CHENG Xian-yi and CAO Xiao-feng   

  • Online:2018-11-14 Published:2018-11-14

摘要: 敏感信息过滤是既重要又复杂的任务。针对当前一些敏感信息过滤模型所存在的时间滞后、准确性低、自适应性差等问题,提出了一个敏感信息自适应多重过滤模型。该模型以互联网中文文本媒体为研究对象,采用意见挖据、机器学习、高性能计算和自然语言处理等技术,从整体和语义角度自适应识别敏感信息。对敏感信息自适应多重过滤模型的研究将为舆情监控、商业智能、辅助决策等应用系统开发提供技术支持。

Abstract: Filtering of network sensitive information is an important and complicated task.Aiming at the problems of detection time lag,low accuracy and poor adaptability,we proposed an adaptive multi filtering model of network sensitive information which is based on the research object of Chinese text media from the internet and uses the technologies of opinion mining,machine learning,high performance computing and natural language processing to recognize the sensitive information adaptively from the point of whole and semantic.The research on adaptive multi filtering model of network sensitive information will give some technical supports for the public opinion monitoring,intelligent business and developing systems of aid making decision application.

[1] http://baike.baidu.com/view/3061484.htm
[2] 李东方.Web2_0环境下互联网信息过滤理论与方法研究[D].合肥:中国科技大学,2009
[3] Greevy E,Alan F S.Classifying racist texts using a support vector machine[C]∥Proceedings of the 27th Annual International ACM SIGIR Conference.New York,NY,USA,2004:468-469
[4] Zhou Y L,Reid E,Qin J.US domestic extremist groups on the Web:link and content analysis[J].IEEE Intelligent Systems,2005,0(5):44-51
[5] Tang Xu-ri,Chen Xiao-he,Qu Wei-guang et al.Semi-Supervised WSD in Selectional Preferences with Semantic Redundancy[C]∥Coling 2010:Poster Volume.Beijing,August 2010:1238-1246
[6] Bermingham A,Smeaton A F.On Using Twitter to Monitor Political Sentiment and Predict Election Results[C]∥Proceedings of the Workshop on Sentiment Analysis where AI meets Psychology (SAAIP)(IJCNLP 2011).Chiang Mai,Thailand,November 2011:2-10
[7] Bermingham A,Smeaton A F.Classifying sentiment in microblogs:is brevity an advantage[C]∥Proceedings of the 19th ACM International Conference on Information and Knowledge -Mana-gement(CIKM ’10).New York,USA,ACM,2010:1833-1836
[8] SIFTsystem[EB/OL].http://www.sift.eom/
[9] Newsweeder[EB/OL].http://eiteseer.ist.psu.ed/lang95newsweeder.html
[10] Dave K,Lawrence S,Pennock D M.Mining the Classification of Product Reviews[C]∥Proceedings of the 12th International World Wide Web Conference.Budapest,Hungary 2003
[11] Wiebe J,Breuce R,Bell M.A Corpus Study of Evaluative and Speculative Language[C]∥Proceedings of the 2nd ACL SIGdial Workshop on Discourse and Dialogue.Aalborg,Denmark:2001 (下转第307页)(上接第275页)
[12] 科技日报.火眼金睛识别红黑网络信息[EB/OL].http://www.stdaily.com/
[13] 贾自艳,何清,张海俊,等.一种基于动态进化模型的事件探测和追踪算法[J].计算机研究与发展,2004,41(7):1273-1280
[14] Zeitzoff T.Using Social Media to Measure Conflict Dynamics[J].Journal of Conflict Resolution,2011,55(6):938-969
[15] http://www.cs.waikato.ac.nz/ml/weka/index_downloading.html

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