基于语言学知识的查询个性化潜力预测  

Predicting Query Potential for Personalization Based on Linguistic Knowledge

作　　者：陈晨[1] 赵铁军[1] 李生[1] 杨沐昀[1] 齐浩亮[2] 

机构地区：[1]哈尔滨工业大学计算机学院,黑龙江哈尔滨150001 [2]黑龙江工程学院计算机系,黑龙江哈尔滨150050

出　　处：《中文信息学报》2012年第6期11-18,共8页Journal of Chinese Information Processing

基　　金：国家自然科学基金重点资助项目(60736044)

摘　　要：大多数关于个性化信息检索的研究都是针对所有查询的,很少有研究试图回答哪些查询将受益于个性化信息检索。从大规模知识库中挖掘大量的语言学知识,用于预测查询的个性化潜力,这些知识包括概念词、歧义词、同义词等。使用语言学知识作为特征,预测查询的个性化潜力,可以减少查询日志的数据稀疏问题的影响。实验结果表明该方法的有效性和可行性。Most of the previous studies on the personalized search are generally designed for all queries, and few have tried to answer which queries can benefit from personalization. In this paper, we mine linguistic knowledge from the large-scale human knowledge base to predict query potential for personalization. The acquired linguistic knowledge includes conceptual terms, ambiguous terms and synonymous terms, which are adopted to design corresponding fea tures for predictive models. The knowledge mined from Wikipedia alleviates the data sparseness of query logs. The experiment results indicate the effectiveness and feasibility of our approach.

关 键 词：查询个性化潜力 语言学知识 查询日志 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]