[1]潘晓英,胡开开,朱静. 一种基于TextRank的文本二次聚类算法[J].计算机技术与发展,2016,26(08):7-11.
　PAN Xiao-ying,HU Kai-kai,ZHU Jing. A Secondary Text Clustering Algorithm Based on TextRank[J].,2016,26(08):7-11.
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 一种基于TextRank的文本二次聚类算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
26
期数:
2016年08期
页码:
7-11
栏目:
智能、算法、系统工程
出版日期:
2016-08-10

文章信息/Info

Title:
 A Secondary Text Clustering Algorithm Based on TextRank
文章编号:
1673-629X（2016)08-0007-05
作者:
 潘晓英; 胡开开; 朱静
 西安邮电大学 计算机学院
Author(s):
 PAN Xiao-ying; HU Kai-kai; ZHU Jing
关键词:
 ; 文本聚类; TextRank ; 提取; 向量空间模型; LDA
Keywords:
 ; text clustering; TextRank; keyword extraction; VSM; LDA
分类号:
TP391.9
文献标志码:
A
摘要:
 针对传统文本聚类技术中存在的聚类精度一般或者运算时间复杂度过高等问题，文中首先介绍了两种较为常用的文本聚类技术：基于划分的K-means和基于主题模型的LDA。在分析各自缺陷的基础上，提出一种基于TextRank的文本二次聚类算法。该算法借鉴主题模型的思想，在传统的聚类过程中引入词聚类，并在关键词提取阶段融合词语的位置与跨度特征，减少了由局部关键词作为全局关键词带来的误差。实验结果表明，改进后的算法在聚类效果上要优于传统的VSM聚类和基于主题模型的LDA算法。
Abstract:
 In view of the existing problems in the traditional text clustering technology,such as the general accuracy or the higher time complexity,two kinds of the commonly used text clustering technology are introduced at first,including K -means based on the division and LDA based on the theme. On the basis of the analysis of their respective defects,a secondary text clustering algorithm based on the TextRank is presented. Reference of idea of theme model,the algorithm introduces the word clustering in the process of traditional cluste-ring,and merges the futures of location and span in the keyword extraction phase,reducing the error by local keywords as global key-words. The experimental results show that the improved algorithm on the cluster effect is superior to the traditional VSM clustering and LDA algorithm based on the theme model.

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更新日期/Last Update: 2016-09-29