• 论文 • 上一篇    

基于加权复杂网络的文本关键词提取

谢凤宏, 张大为, 黄丹, 谢福鼎   

  1. 辽宁师范大学计算机与信息技术学院, 大连 116081
  • 收稿日期:2010-09-15 修回日期:1900-01-01 出版日期:2010-11-25 发布日期:2010-11-25

谢凤宏;张大为;黄丹;谢福鼎. 基于加权复杂网络的文本关键词提取[J]. 系统科学与数学, 2010, 30(11): 1592-1596.

XIE Fenghong;ZHANG Dawei;HUANG Dan;XIE Fuding. Keywords Extraction Based on Weighted Complex Network[J]. Journal of Systems Science and Mathematical Sciences, 2010, 30(11): 1592-1596.

Keywords Extraction Based on Weighted Complex Network

XIE Fenghong, ZHANG Dawei, HUANG Dan, XIE Fuding   

  1. College of Computer and Information Technology, Liaoning Normal University, Dalian 116081
  • Received:2010-09-15 Revised:1900-01-01 Online:2010-11-25 Published:2010-11-25
通过分析基于复杂网络的关键词提取算法的特点和不足,提出了一种基于加权复杂网络提取的文本关键词新算法.首先根据文本特征词之间的关系构建文本的加权复杂网络模型,其次通过节点的加权聚类系数和节点的介数计算节点的综合特征值,最后根据综合特征值提取出文本关键词.实验结果表明,
该算法提取的关键词能够较好地体现文本主题,提取关键词的准确率比已有算法有明显提高.
By analyzing the characteristics and disadvantages of the existing keywords extraction algorithms based on complex network, a new keywords extraction
algorithm is proposed by using of weighted complex network. First of all, a weighted complex network model is constructed according to the relationship between the feature words of text. Secondly, the weighted clustering coefficient and betweeness are introduced to calculate the node's multi-feature value. Finally, the keywords are extracted by the multi-feature value. The experiment results show that the keywords extracted by this algorithm have great contribution to the text subject, and the accuracy of keywords extraction is better than the existing algorithms.

MR(2010)主题分类: 

()
[1] 叶子诚, 闫桂英. 基于图模型的关键词提取算法研究[J]. 系统科学与数学, 2021, 41(4): 967-975.
阅读次数
全文


摘要