查询词相似度加权的邻近性检索方法  

A Semantic Similarity Weighted Query Term Proximity Framework for Information Retrieval

作　　者：乔亚男[1,2] 刘跃虎[1] 齐勇[3] 

机构地区：[1]西安交通大学电子与信息工程学院,西安710049 [2]南京大学计算机软件新技术国家重点实验室,南京210093 [3]西安交通大学电子与信息工程学,院西安710049

出　　处：《模式识别与人工智能》2013年第2期189-194,共6页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.61202181);国家自然科学基金重点项目(No.60933003);博士后科学基金项目(No.2012M512006);中央高校基本科研业务费专项资金项目资助

摘　　要：传统的邻近性检索模型同等地看待所有查询词,不加区分地考虑所有查询词的邻近性,造成"平行概念效应",影响邻近性检索方法的性能.文中提出一种查询词相似度加权的邻近性检索方法.该方法根据查询词之间的语义相似度对查询词邻近性统计量加权,可进一步推断用户的实际信息需求,挖掘查询中蕴含的更深层次的信息.实验结果表明,在短查询较多的应用环境下,文中方法可较显著提升传统邻近性检索模型的性能,有效规避查询词邻近性的平行概念效应.Traditional proximity retrieval models treat query terms equall proximities between query terms. Thus, the parallel concept effect y and they do not distinguish the is caused, and the performance of many query term proximity based information retrieval models is affected. A semantic similarity weighted query term proximity framework is proposed. The statistics of query term proximity are weighted in this framework by the semantic similarities between query terms, and then the in-depth information needs can be concluded and mined. Experimental results show that compared with traditional proximity retrieval models, the proposed framework greatly improves the performance of traditional proximity retrieval models and avoids the parallel concept effect efficiently for short queries.

关 键 词：信息检索 语义相似度 查询词邻近性(QTP) 信息需求 

分 类 号：TP391.3[自动化与计算机技术—计算机应用技术]