Term co-occurrence and context window-based combined approach for query expansion with the semantic notion of terms

In this paper, our focus is to capture the limitations of Pseudo-Relevance Feedback (PRF) based query expansion (QE) and propose a hybrid method to improve the performance of PRF-based QE by combining corpus-based term co-occurrence information, context window of query terms and semantic information of term. Firstly, the paper suggests use of various corpus-based term co-occurrence approaches to select an optimal combination of query terms from a pool of terms obtained using PRF-based QE. Third, we use semantic similarity approach to rank the QE terms obtained from top feedback documents. Fourth, we combine co-occurrence, context window and semantic similarity based approaches together to select the best expansion for query reformulation. The experiments were performed on FIRE ad-hoc and TREC-3 benchmark datasets of information retrieval task. The results show significant improvement in terms of precision, recall and mean average precision (MAP). This experiment shows that the combination of various techniques in an intelligent way gives us goodness of all of them.