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基于领域词义关联的研究
作者:王忠振 王涛 杜晓莉
来源:本站原创
更新时间:2014-1-14 11:04:00
正文:

                               (1.国防科技大学计算机学院,湖南省长沙市 410000;
                                      2.国防科技大学计算机学院,湖南省长沙市 410000;
                                      3.国防科技大学计算机学院,湖南省长沙市 410000)

[摘  要]:领域词义关联是在语义上对特定领域内的语言进行基本单位关联性计算,而且作为关联性计算的基础,在其他级别的文本间关系度量中发挥着非常重要的作用。领域词义关联的研究具有独特的语言特征,依据维基百科和百度百科不仅对每个词条有详细的解释说明,还对说明中相关属性词条进行链接,可以准确推测特征词的有效语言特征,利用深度学习算法充分挖掘与利用词汇属性之间的关联性。
[关键词]:词义关联,深度学习,维基百科
中图分类号:TP391.1     文献标识码:A        文章编号:

                              A Research Based on Domain Meaning Relevance

                                   WANG Zhongzhen1  WANG Tao2  DU Xiaoli3 
  (1. National University of Defense Technology,Changsha 410000,china. WANG Zhongzhen,54696661@qq.com
  2.National University of Defense Technology,Changsha 410000,china. WANG Tao,631570216@qq.com
  3.National University of Defense Technology,Changsha 410000,china. Du Xiaoli,821979047@qq.com)

Abstract:Domain Meaning Relevance (DMR) refers to computation of the semantic relevance of basic language units in a specific domain. As the fundamental for relevance computation, DMR plays a significant role in measurement of relevance between texts of different levels. Research of DMR has particular Linguistic characteristics. Wikipedia and Baidupedia give not only detailed explanation for each entry, but also links to entries of similar attributes. By this advantage, we can use deep learning algorithm to exploit mining and utilizing the relevance between attributes, and infer the linguistic features of Feature Words.
Key words:Semantic Relevance, Deep Learning,Wikipedia

 

 

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作者简介:
  王忠振,男,1980年6月出生,北京昌平人,国防科学技术大学计算机学院计算机科学与技术专业工程硕士。主要研究方向为数据挖掘、自然语言处理和信息安全。
  王涛,男,1979年9月出生,河南长葛人,国防科学技术大学计算机学院计算机科学与技术专业工程硕士。主要研究方向为数据挖掘、微博意见领袖和舆情控制。
  杜晓莉,女,1989年12月生,河北石家庄人,国防科学技术大学分布与并行处理国家重点实验室。主要研究方向为社会网络与移动计算、移动无线通信。
  

 
 
   
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