基于统计和特征相结合的查询纠错方法研究  

Auto-Correction Search Model Based on Statistics and Characteristics

作　　者：段建勇[1] 关晓龙[1] 

机构地区：[1]北方工业大学计算机学院,北京100144

出　　处：《现代图书情报技术》2016年第2期34-42,共9页New Technology of Library and Information Service

基　　金：北京市社会科学基金项目"北京市公共危机事件在网络传播中的演化机制与模型研究"(项目编号:13SHC031);国家自然科学基金项目"面向维基百科的多粒度一体化信息抽取方法研究"(项目编号:61103112)的研究成果之一

摘　　要：【目的】提高搜索引擎查询纠错过程中的准确率和召回率,改善用户的检索体验。【方法】提出一种基于统计和特征相结合的查询纠错模型,建立混淆集生成模型,将用户输入的查询关键字生成其对应的混淆集;建立混淆集排序模型,对混淆集中的词条进行排序,选出混淆集中最佳的词条与用户输入的查询关键字对照,以此达到查错纠错的目的。【结果】实验结果证明该模型在搜索引擎查询时具有较好的效果,测试集在110k时的准确率和召回率分别达到92.2%和95%,相对于N-gram纠错模型准确率和召回率分别提高13.6%和8.3%。【局限】该模型中混淆集的生成规则有限、模型的训练需要大量的计算。【结论】本模型能够提高搜索引擎查询的准确率及效率,改善用户的检索体验。[Objective] This study aims to improve the precision, recall and user experience of the search engine. [Methods] We proposed an automatic query correction model based on the statistics and characteristics. First, established a model to generate the confusion query set for the users’ search terms, Then, created a ranking algorithm for the confusion set and chose the best match for the original queries. [Results] Our new model improved the search engine’s performance. The precision and recall rates were 92.2% and 95% on a testing set of 110 k, which were 13.6% and 8.3% higher than those of the N-gram model. [Limitations] Our model only generated four types of words for the confusion set, and the training process required a lot of computation. [Conclusions] The new model can improve the precision, recall and user experience of the search engine.

关 键 词：查询纠错 混淆集 N-GRAM模型 N-gram相似度 编辑距离 点击词频 

分 类 号：TP391.3[自动化与计算机技术—计算机应用技术]