期刊文献+

一种Web信息挖掘的英语阅读选篇分类研究 被引量:2

Research on Web Information Mining in English Reading Materials Classification
下载PDF
导出
摘要 随着网络信息化程度的不断提高,英语阅读教学模式也发生了根本性的变化,网络环境下英语阅读教学题材选篇的多样性与灵活性有了质的飞跃。由于目前英语阅读题材选篇多为手工挑选,题材涉及面不够广泛并且任务繁重,提出了一种基于Web信息挖掘的英语阅读选篇自动分类的设计模式,以期利用计算机技术为构建高效实用的英语阅读自动选篇系统提供有效的解决途径。 With the development of information technology and increasing web pages in web resources, the mode of English reading teaching has changed greatly, and the diversity and flexibility of English reading materials achieve a great progress. Because English reading materials were hand selection, subjects were lack of varieties and the classification work was heavy, this paper provides a model of automation classification of English reading materials based on web information mining. Through this method, it will provide an effective method for building a highly efficient and practical system of automatic classification of English reading materials.
作者 吴昊
出处 《现代教育技术》 CSSCI 2009年第2期67-70,共4页 Modern Educational Technology
基金 湖南省教育科学"十一五"规划2006年度重点资助课题(课题编号:XJK06AXJ009)
关键词 WEB信息挖掘 英语阅读 文本分类 结构模型 web information mining English reading text categorization structure model
  • 相关文献

参考文献9

  • 1Iawei Han and Micheline Kamber, Data Mining: Concepts and Techniques[J].Morgan Kanfmann Publishers, 2001
  • 2Olivier Vandecruys, David Martens, Bart Baesens, Christophe Mues, Manu De Backer, Raf Haesen, Mining Software Repositories for Comprehensible Software Fault Prediction Models Journal of Systems and Software Vol. 81, Nb. 5, pp. 823-839, 2008
  • 3BAI Jing, NIE Jianyun, CAO Guihong. Integrating compound terms in Bayesian text classification[C]//Proc of IEEE /W IC/ACM International Conference. 2005: 598-601.
  • 4LI Baoli, LU Q, YU Shiwen. An adaptive k-nearest neighbor text categorization strategy[J].ACM Transactions on Asian Language Information Processing,2004,12(31):215-226.
  • 5E.Kirkos,C.Spathis and Y. Manolopoulos, Applying data mining methodologies for auditor selection, Proceedings 11th Pan-Hellenic Conference in Informatics (PCI), Patras, Greece, 2007, pp. 165-178.
  • 6Magdalini Eirinaki, Michalis Vazirgiannis, Web Mining for Web Personalization [J].ACM Transactions on Internet Technology, 2003.
  • 7He B,Tao T, Chang K. Clustering structured Web sources: A schema-based,model-differentiationapproach[A].Intemational Workshop on Clustering Information over the Web [C]. Crete, Greece, 2004.
  • 8MODHA D S, SPAN GL ER W S. Feature weighting in K-Means clustering[J]. Machine Learning, 2003, 52(3): 217-237.
  • 9Ma ZhongMiing, Gautam Pant, Olivia R Sheng. Interest-Based Personalized Search [C]//ACM Transactions on Information Systems. New York: ACM, 2007.

同被引文献35

引证文献2

二级引证文献34

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部