基于矩阵加权关联规则的跨语言查询译后扩展

doi:10.16451/j.cnki.issn1003-6059.201810003

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Abstract

A computing method for matrix-weighted itemset support is proposed firstly, and the algorithm of matrix-weighted association patterns mining for cross-language query expansion is presented. Then, the algorithm of cross-language query post-translation expansion is put forward based on matrix-weighted association rules mining. The first cross-language retrieval is performed to obtain the top initially retrieved documents(TIRDs) by machine translation, and the relevance feedback documents(RFDs) are gained from TIRDs by user correlation judgment. The matrix-weighted frequent itemsets containing original query terms are mined from RFDs by means of computing support and the association rules with original query terms are extracted from frequent itemsets according to the evaluation framework of confidence-interest. To implement cross-language query post-translation expansion, the consequents or antecedents of the rules are treated as expansion terms and the importance of the expansion terms is measured by the confidence and interest of the rule. Experiments on NTCIR-5 CLIR standard test set show that the proposed algorithm improves the performance of cross-language query expansion, and it is beneficial in cross-language retrieval of long queries. The performance of post-translation consequent expansion is better than that of the antecedent one.

Received: 08 May 2018

ZTFLH:

TP 311

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Supported by National Natural Science Foundation of China(No.61762006,61662003,61262028)

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	Articles by authors
	HUANG Mingxuan
	JIANG Caoqing
	HE Donglei

Cite this article:

HUANG Mingxuan,JIANG Caoqing,HE Donglei. Cross Language Query Post-Translation Expansion Based on Matrix-Weighted Association Rules[J]. , 2018, 31(10): 887-898.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201810003 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2018/V31/I10/887

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