Cross Language Query Post-Translation Expansion Based on Matrix-Weighted Association Rules
HUANG Mingxuan1,2, JIANG Caoqing1,2, HE Donglei1,2
1.Guangxi Key Laboratory Cultivation Base of Cross-Border E-commerce Intelligent information Processing, Guangxi University of Finance and Economics, Nanning 530003
2.School of Information and Statistics, Guangxi University of Finance and Economics, Nanning 530003
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.
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2018, Vol. 31 
