A SMT-based approach for query expansion in information retrieval

In practical applications of information retrieval, such as the search engine, the query user submitted contains only several keywords usually. This will cause unmatched issue of word of relevant files and user's query and have more serious negative effects on the performance of information retrieval. On the basis of analyzing of process of producing query, this paper puts forward a new method of query expansion on the basis of model of statistical machine translation. The approach extract related terms between documents and query through statistical machine translation model, then expand into query. The experiment result on TREC data collection shows the proposed method, SMT-based query expansion, has 12-17% of the improvement all the time more than the language model method without expanding. Compared to the popular approach of query expansion, pseudo feedback, the proposed method has the competed average precision.