基于分段估计和PageRank的文本信息相似性搜索算法

Text information similarity search algorithm based on segment estimation and PageRank

由于当前已有方法未能考虑文本信息特征提取问题,导致平均相关性、平均优异率和新词搜索准确率下降。为了有效解决上述问题,提出一种基于分段估计和PageRank的文本信息相似性搜索算法。采用分段估计法对文本特征进行提取;将PageRank取值大小作为文本信息初步分类的准则,计算文本信息不同特征的相似度,将文本信息相似度进行排序,根据文本信息间的相关性搜索相似性文本信息,有效实现了文本信息相似性搜索。仿真实验结果表明,本文算法能够全面提升平均相关性、平均优异率和新词搜索准确率,其新词搜索准确率最高值达到了98.98%。

Because the current existing methods fail to consider the problem of text information feature extraction,the average relevance,average excellence rate and new word search accuracy rate decrease. In order to effectively solve the above problems,a text information similarity search algorithm based on segment estimation and PageRank is proposed. First,the segmentation estimation method is used to extract text features,and the PageRank value is taken as the criterion for preliminary classification of text information. Then,the similarity of different features of text information is calculated,the text information similarity is sorted. Finally,the similarity is searched based on the relevance between text information,effectively realizing the similarity search of text information. Simulation results show that the proposed algorithm can comprehensively improve the average correlation,average excellence rate and new word search accuracy,and the highest new word search accuracy reaches 98.98%,which indicates that the algorithm can obtain high quality and high stability search results.