基于项权值排序挖掘的跨语言查询扩展  

Cross Language Query Expansion Based on Item Weight Sorting Mining

作　　者：黄名选[1,2,3] 蒋曹清 HUANG Ming-xuan;JIANG Cao-qing(Guangxi Key Laboratory of Cross-border E-commerce Intelligent Information Processing,Guangxi University of Finance and Economics,Nanning,Guangxi 530003,China;Guangxi(ASEAN)Financial Research Center,Guangxi University of Finance and Economics,Nanning,Guangxi 530003,China;School of Information and Statistics,Guangxi University of Finance and Economics,Nanning,Guangxi 530003,China)

机构地区：[1]广西跨境电商智能信息处理重点实验室(广西财经学院),广西南宁530003 [2]广西财经学院广西(东盟)财经研究中心,广西南宁530003 [3]广西财经学院信息与统计学院,广西南宁530003

出　　处：《电子学报》2020年第3期568-576,共9页Acta Electronica Sinica

基　　金：国家自然科学基金(No.61762006,No.61662003);广西应用经济学一流学科(培育)开放性课题(No.2018MA07);广西(东盟)财经研究中心开放性课题(No.2018DMCJYB08)。

摘　　要：为了改善自然语言处理应用中长期存在的主题漂移和词不匹配问题,本文首先提出一种加权项集支持度计算方法和基于项权值排序的剪枝方法,给出面向查询扩展的基于项权值排序的加权关联规则挖掘算法,讨论关联规则混合扩展、后件扩展和前件扩展模型,最后提出基于项权值排序挖掘的跨语言查询扩展算法.该算法采用新的支持度和剪枝策略挖掘加权关联规则,根据扩展模型从规则中提取高质量扩展词实现跨语言查询扩展.实验结果表明,与现有基于加权关联规则挖掘的跨语言扩展算法比较,本文扩展算法能有效遏制查询主题漂移和词不匹配问题,可用于各种语言的信息检索以改善检索性能,扩展模型中后件扩展获得最优检索性能,混合扩展的检索性能不如后件扩展和前件扩展,支持度对后件扩展更有效,置信度更有利于提升前件扩展和混合扩展的检索性能.本文挖掘方法可用于文本挖掘、商务数据挖掘和推荐系统以提高其挖掘性能.To ameliorate the long-standing problems of theme drift and word mismatch in natural language processing applications,this paper first proposes a computing method for weighted itemset support and a pruning method based on item weight sorting(IWS).And then,a weighted association rule mining algorithm for query expansion is presented based on the IWS,and the models such as association rule antecedent and consequent hybrid expansion(RACHE),rule consequent expansion(RCE)along with rule antecedent expansion(RAE)are discussed.Finally,an algorithm of cross-language query expansion(CLQE)is put forward based on the IWS mining.The algorithm utilized the new support and the pruning method to mine the weighted association rules,and extracted high quality expansion terms from the rules according to the expansion models in order to carry out CLQE.A comparison between the proposed expansion algorithm and the existing CLQE algorithms based on weighted association rules mining is made,which shows that the former can effectively restrain the problems of query topic drift and word mismatch,and can be used in information retrieval in various languages to improve retrieval performance.The RCE achieves the optimal retrieval performance in the proposed expansion models,and the retrieval performance of the RACHE is not as good as that of the RAE and the RCE.The support is more effective for the RCE algorithm.The confidence can make the RAE and the RACHE get the best retrieval result.And moreover,the proposed mining method can be used in text mining,business data mining and recommendation system to improve its mining performance.

关 键 词：自然语言处理 文本挖掘 信息检索 跨语言检索 查询扩展 推荐系统 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]