基于语词抽取与负关联规则挖掘的信息检索

Information Retrieval Based on Terms Extraction and Negative Association Rules Mining

将语词抽取、负关联规则挖掘和查询扩展技术应用于信息检索,提出一种基于语词抽取与负关联规则挖掘融合的信息检索系统模型及其算法。详细论述模型的设计思想、各模块的功能,以及模型的理论分析和检索算法。该模型能够将语词抽取、负关联规则挖掘和查询扩展三种技术融合,对初检文档集进行有效地处理,得到高质量的与原查询词相关的扩展词,和原查询组合成新查询,再进行二次检索,有效地解决了词不匹配的问题。实验结果表明,该模型有效,能改善和提高信息检索性能。

In order to apply terms extraction and negative association rules mining, as well as query expansion technique to information retrieval, a novel information retrieval system model and algorithm is introduced based on terms extraction and negative association rules mining. Its design concePtion and the function of each module is expounded. Its theoretical analysis for implementation and searching algorithm is also expatiated. The model can integrate terms extraction and negative association rules mining as well as query expansion three kinds of technologies and deal with effectively the top-ranked retrieved local documents to obtain high-quality expansion terms related to the original query terms. And then, the expansion terms are combined with original query to carry on the second retrieval again and the term mismatch issue of existing information retrieval is solved availably. The results of the experiment show that the proposed model can effectively improve and enhance the information retrieval performance.