期刊文献+

基于随机游走模型的维基百科语义关系研究

Study of Semantic Relations in Wikipedia Based on the Random Walk Model
下载PDF
导出
摘要 运用随机游走模型提出了一种基于维基百科的语义相关度的计算方法。维基百科中包含了丰富的链接结构,这些链接结构一定程度上能够反应词条之间概念上的相关性,以内容链接和外部链接关系来计算基于维基百科的语义相关度,并在WS-353数据集上进行了实验,取得了较好的准确性。 In this paper, the random walk model is proposed based on the Wikipedia semantic relatedness calculation. Wikipedia contains a wealth of link structure, these link structures can react correlation between the conceps to some extent, this paper calculates the semantic correlation of Wikipedia against text links and exterlinks, WS-353 data setsis used to test, and gained a better accuracy.
作者 李志萍
出处 《电脑编程技巧与维护》 2014年第4期6-8,17,共4页 Computer Programming Skills & Maintenance
关键词 随机游走 维基百科 链接结构、语义相关度 random walk Wikipedia link structure semantic relatedness
  • 相关文献

参考文献15

  • 1孙琛琛,申德荣,单菁,聂铁铮,于戈.WSR:一种基于维基百科结构信息的语义关联度计算算法[J].计算机学报,2012,35(11):2361-2370. 被引量:26
  • 2BUDANITSKYA,HIRSTG,Evaluating wordnet-based measures of lexical semantic relatedness [J] . Computional Linguitic, 2006, 32 (1) :13-47.
  • 3GRINEVA M,LIZORKIN D. Extracting key terms from noisy and muhitheme documents [C] //Proceedings of the 18th in- ternational conference on World wide web:: [S.I.] : [s.n.] 2009:661-670.
  • 4GABRILOVICH E,MARKOVITCH S.Computing semantic re- latedness usingWikipedia- based explicit semantic analysis [C] //The 20th International JointConference on Artificial In- telligence (IJCAI) . Hyderabad, India: [s.n] ,2007:1606 1611.
  • 5STRUBE M,PONIETFO SP. WikiRelate! Computing semantic relatedness usingWikipedia [C] //rhe 21st National Conference on Artificial Intelligence. Boston,MA: [s.n], 2006:1419-1424.
  • 6SeeoN.VealeT.HayesJ.;2004;AnlntrinsielnformationContentMe trieforSemantieSimilarityinW6rdNet;InProeofECAI.
  • 7DavidMilne , ComputingSemantieRelatednessusingWikiPedia Link Structure.
  • 8李赞.基于中文维基百科的语义知识挖掘相关研究[D].北京:北京邮电大学,2009.
  • 9Michael Strube,SimonePaolo Ponzetto. WikiRelate! Comput- ing SemanticRelatedness Using Wikipedia. In: Anthony Cohn, University of Leeds, eds.Proceedings of the 21th American Association for Artificial Intelligence, Boston:AAAI Press, 2006: 1419-1424.
  • 10Sheldon M.Ross.Introduction to Probability Models [M] .pt-press,2007:198,224-230.

二级参考文献16

  • 1Buchanan B G, Feigenbaum E A. Forward//Davis R, Lenat D B.Knowledge-Based Systems in Artificial Intelligence. New York: McGraw-Hill, 1982:39-51.
  • 2Lenat D, Guha R. Building Large Knowledge Based Systems. New York: Addison Wesley, 1990.
  • 3Ricardb B Y, Berthier R N. Modern Information Retrieval. New York: Addison Wesley, 1999.
  • 4Deerwester S, Dumais S, Furnas G, Landauer T, Harshman R. Indexing by latent semantic analysis. Journal of the American Society for Information Science, 1990, 41(6): 391-407.
  • 5Alexander B, Graeme H. Evaluating wordnevbased measures of lexical semantic relatedness. Computational Linguistics, 2006, 32(1): 13-47.
  • 6Mario J. Roget's thesaurus as a lexlcal resource for natural language processing [Ph. D. dissertation]. University of Ottawa, Ottawa, 2003.
  • 7Milne D, Witten I H. An effective, low-cost measure of semantic relatedness obtained from Wikipedia links// Proceedings of the 23th Association for the Advancement of Artificial Intelligence. Chicago, US, 2008:25-30.
  • 8Philip R. Using information content to evaluate semantic similarity in a taxonomy//Proceedings of the 14th Interna tional Joint Conference on Artificial Intelligence. Montreal, Canada, 1995:448-453.
  • 9Mario J, Stan S. Roger's thesaurus and semantic similarity// Proceedings of Conference on Recent Advances in Natural Language Processing. Borovets, Bulgaria, 2003: 212-219.
  • 10Li Yun. Mining semantic knowledge from Chinese Wikipedia [Ph. D. dissertation]. Beijing University of Posts and Telecommunications, Beijing, 2009.

共引文献26

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