基于概念树剪枝的 LCA 查询扩展

计算机科学 ›› 2015, Vol. 42 ›› Issue (Z6): 479-483.

基于概念树剪枝的 LCA 查询扩展

李卫疆,王锋

昆明理工大学信息工程与自动化学院昆明650500,昆明理工大学信息工程与自动化学院昆明650500

出版日期:2018-11-14 发布日期:2018-11-14
基金资助:
本文受国家自然科学基金项目:基于统计机器翻译和自动文摘的查询扩展研究(61363045)资助

Method of Query Expansion Based on LCA Prune Semantic Tree

LI Wei-jiang and WANG Feng

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

摘要/Abstract

摘要： 在信息检索应用实践中存在用户表达查询请求不够准确、文档与查询词不匹配以及查询优化等问题。针对这些问题,提出了基于概念树剪枝的 LCA 查询扩展方法,这种混合的查询扩展技术综合了语义和局部上下文分析这两种查询扩展方法,利用 LCA 方法检索得到的扩展词集对语义词典构造的概念树进行适当剪枝,以弥补概念树的不足,并对扩展词候选集用改进的算法重新分配权重。在 TREC 数据集的实验结果表明:与单独基于统计或者基于语义的查询扩展方法相比, 基于概念树剪枝的 LCA 查询扩展方法性能有较大提高。

Abstract: Searching and finding out useful information from a mass stock in a very short time is becoming a tough tax and many times the user will have to receive a lot of information that may not appear any useful for the users.Problems mainly come from the query which users have provided without enough accuracy,the mismatches of the queries and the expression of the documents,or query optimization.To deal with these problems,this paper proposed a novel hybrid query expansion method which synthesizes the merits of semantic query expansion and local context analysis(LCA).Firstly,we retrieved the documents by LCA method,then used these terms to trim the semantic tree,and calculated the weight of expansion term based on this improved algorithm.We compared the effectiveness of these approaches.And the results show that,although local context analysis has some advantages,the LCA prune semantic tree yields better performance than the techniques on the simple query expansion.

Key words: Query expansion,Local context analysis,Concept tree,Pruning,Relevance algorithm

李卫疆,王锋. 基于概念树剪枝的 LCA 查询扩展[J]. 计算机科学, 2015, 42(Z6): 479-483. https://doi.org/

LI Wei-jiang and WANG Feng. Method of Query Expansion Based on LCA Prune Semantic Tree[J]. Computer Science, 2015, 42(Z6): 479-483. https://doi.org/

参考文献

[1] Sanjuan E,Ibekwe-Sanjuan F.Combining language models with NLP and interactive query expansion[C]∥Proceedings of the Focused Retrieval and Evaluation,and 8th International Confe-rence on Initiative for the Evaluation of XML Retrieval.Berlin,Heidelberg:Springer-Verlag,2010:122-132
[2] Rocchio J J.Document Retrieval Systems-Optimization andEvaluation[D].Harvard,1966
[3] Robertson S E,Jones K S.Relevance weighting of search terms[J].Journal of the American Society for Information Science,1976,27(3):129-146
[4] Attar R,Fraenkel A S.Local feedback in full-text retrieval sys-tems[J].Journal of the Association for Computing Machinery,1977,24(3):397-417
[5] Wu H,Salton G.The estimation of term relevance weights using relevance feedback[J].Journal of Documentation,1981,37(4):194-214
[6] Qiu Y,Frei H P.Concept based query expansion[C]∥Procee-ding of the 16th annual international ACM SIGIR Conference on Research and Development in Information Retrieval.1993:160-169
[7] Crouch C J,Yang B.Experiments in automatic statistical thesaurus construction[C]∥Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval,1992.ACM,1992:77-88
[8] Xu J,Croft W B.Query expansion using local and global document analysis[C]∥Proceeding of Annual International ACM Sigir Conference on Research & Development in Information Retrieval.1996:4-11
[9] Xu J,Croft B.Improving the effectiveness of information re-trieval with local context analysis[J].ACM Transaction on Information Systems,2000,18(1):79-112
[10] Kelly D,Teevan J.Implicit feedback for inferring user prefe-rence:a bibliography[C]∥ACM SIGIR Forum.2003:18-28
[11] Shen X,Tan B,Zhai C.Context-sensitive information retrievalusing implicit feedback[C]∥Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval.2005:43-50
[12] Jones K S.Automatic keyword classification for information retrieval[M].1971
[13] Jing Y F,Croft W B.An association thesaurus for information retrieval[C]∥RIAO Conference Proceedings.1994:146-160
[14] Yahia S B,Jaoua A.Discovering knowledge from fuzzy concept lattice[M]∥Data mining and computational intelligence.Sprin-ger,2001:167-190
[15] Fonseca B M,Golgher P B,De Moura E S,et al.Discoveringsearch engine related queries using association rules[J].Journal of Web Engineering,2003,2(4):215-227
[16] Latiri C C,Yahia S B,Chevallet J P,et al.Query expansion using fuzzy association rules between terms[J].Proceedings of JIM.2003
[17] Cui H,Wen J,Nie J,et al.Query expansion by mining user logs[J].IEEE Transactions on Knowledge and Data Engineering,2003,15(4):829-839

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