融合Copulas理论和关联规则挖掘的查询扩展

将Copulas理论引入文本特征词关联模式挖掘,提出融合Copulas理论和关联规则挖掘的查询扩展算法.从初检文档集中提取前列n篇文档构建伪相关反馈文档集或用户相关反馈文档集,利用基于Copulas理论的支持度和置信度对相关反馈文档集挖掘含有原查询词项的特征词频繁项集和关联规则模式,从这些规则模式中提取扩展词,实现查询扩展.在NTCIR-5 CLIR中英文本语料上的实验表明,文中算法可有效遏制查询主题漂移和词不匹配问题,改善信息检索性能,提升扩展词质量,减少无效扩展词.

Query Expansion Combining Copulas Theory and Association Rules Mining

The Copulas theory is introduced into the association pattern mining of text feature terms, and a query expansion algorithm combining Copulas theory and association rules mining is proposed. Firstly, top n documents of the document set returned by the query are extracted to construct the pseudo-relevance feedback document set (PRFDS) or user relevance feedback document set(URFDS). Then, the support and the confidence based on Copulas theory are applied to mine the feature term frequent itemsets and association rule patterns with the original query terms in PRFDS or URFDS, and the expansion terms are obtained from the patterns to realize query expansion. The experimental results on NTCIR-5 CLIR Chinese and English corpus show that the proposed expansion algorithm effectively restrains the problems of query topic drift and word mismatch, and enhances the performance of information retrieval with the quality of expansion terms improved and the invalid expansion terms reduced.