计算机科学 ›› 2015, Vol. 42 ›› Issue (6): 18-22.doi: 10.11896/j.issn.1002-137X.2015.06.004

• 综述 • 上一篇    下一篇

基于分类模型的查询扩展方法

李维银,石玉龙,陈杰,施重阳   

  1. 北京理工大学计算机学院 北京100081,北京理工大学计算机学院 北京100081,北京理工大学计算机学院 北京100081,北京理工大学计算机学院 北京100081
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受中国科学院自动化研究所复杂系统管理与控制国家重点实验室开放课题(99S9021F4D),国家自然科学基金(61472034),教育部新世纪优秀人才支持计划(NCET-13-0041),北京理工大学基础研究基金资助

Query Expansion Based on Classification Model

LI Wei-yin, SHI Yu-long, CHEN Jie and SHI Chong-yang   

  • Online:2018-11-14 Published:2018-11-14

摘要: 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。

关键词: 查询扩展,分类模型,信息检索,伪相关反馈

Abstract: 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.

Key words: Query expansion,Classification model,Information retrieval,Pseudo relevance feedback

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