Topic drift and word mismatch are a difficult problem in natural language processing. The combination of text mining and information retrieval can help to solve the problem. In view of this

this paper proposes an algorithm of Vietnamese-English cross language (VECL) query post-translation expansion based on all-weighted positive and negative association pattern mining. The algorithm utilized a computing method of support and correlation degree of all-weighted positive and negative itemset

and mined the all-weighted positive and negative association pattern related to the original query by the pattern evaluation framework in the user relevance feedback document set from the VECL first retrieval results. The expansion terms were extracted from the patterns in order to carry out VECL query post-translation expansion. A comparison between the proposed algorithm and the existing cross language query expansion algorithms based on pseudo relevance feedback and weighted association pattern mining is made

which shows that the former can effectively reduce the problems of query topic drift and word mismatch

and improve the performance of cross language information retrieval. And moreover

the method of pattern mining in this paper can be used in recommender systems and improve its accuracy.