基于分类模型的查询扩展方法

查询扩展作为查询优化的重要组成部分,对改善信息检索系统的性能起到了至关重要的作用.传统的伪相关反馈查询扩展方法虽然在一定程度上提高了检索性能,但选择的扩展词中会包含一部分与原查询不相关的词语,这对检索性能的提升产生了不利影响.提出了一种基于分类模型的查询扩展方法,该算法综合候选扩展词的统计信息和多种特征,采用朴素贝叶斯分类模型对初次得到的候选扩展词进行再次分类选择,进一步去除与查询词相关性小的扩展词.在TREC 2013数据集上的实验结果表明,提出的查询扩展方法能够有效提高用户查询的查准率和查全率.

Query Expansion Based on Classification Model

As a key component of query optimization,query expansion plays an important role in improving the perfor-mance of information retrieval systems.Traditional query expansion methods on pseudo-relevance feedback improve the performance of retrieval to some extent.However,the selected expansion terms will also include some irrelevant ones,which leads to adverse effect.In this paper,a novel query expansion method based on classification model was proposed.Combining with statistical information and various features of the candidate expansion terms,this method employs Naive Bayes classification model to reselect the candidate expansion terms so as to further filter the irrelevant ones.Experimental results on TREC 2013 datasets show that the proposed query expansion method can efficiently improve the precision and recall of user queries.