Relevance Feedback-based Query Expansion Model using Ranks Combining and Word2Vec Approach

Abstract

ABSTRACTQuery expansion is a well-known method for improving the performance of information retrieval systems. Pseudo-relevance feedback (PRF)-based query expansion is a type of query expansion approach that assumes the top-ranked retrieved documents are relevant. The addition of all the terms of PRF documents is not important or appropriate for expanding the original user query. Hence, the selection of proper expansion term is very important for improving retrieval system performance. Various individual query expansion term selection methods have been widely investigated for improving system performance. Every individual expansion term selection method has its own weaknesses and strengths. In order to minimize the weaknesses and utilizing the strengths of the individual method, we used multiple terms selection methods together. First, this paper explored the possibility of improving overall system performance by using individual query expansion terms selection methods. Further, ranks-aggregating method named Borda count is used for combining multiple query expansion terms selection methods. Finally, Word2vec approach is used to select semantically similar terms with query after applying Borda count rank combining approach. Our experimental results on both data-sets TREC and FIRE demonstrated that our proposed approaches achieved significant improvement over each individual terms selection method and other's related state-of-the-art method.