What is Drift Query

The quality of retrieval documents in CLIR is often poor compared to IR system due to (1) query mismatching, (2) multiple representations of query terms, and (3) un-translated query terms. The inappropriate translation may lead to poor quality of results. Hence, automated query translation is performed using the back-translation approach for improvement of query translation. This chapter mainly focuses on query expansion (Q.E) and proposes an algorithm to address the drift query issue for Hindi-English CLIR. The system uses FIRE datasets and a set of 50 queries of Hindi language for evaluation. The purpose of a term ordering-based algorithm is to resolve the drift query issue in Q.E. The result shows that the relevancy of Hindi-English CLIR is improved by performing Q.E. using a term ordering-based algorithm. The outcome achieved 60.18% accuracy of results where Q.E has been performed using a term ordering based algorithm, whereas the result of Q.E without a term ordering-based algorithm stands at 57.46%.