Co-occurrence and Semantic Similarity Based Hybrid Approach for Improving Automatic Query Expansion in Information Retrieval

Pseudo Relevance feedback (PRF) based query expansion approaches assumes that the top ranked retrieved documents are relevant. But this assumption is not always true; it may also possible that a PRF document may contain different topics, which may or may not be relevant to the query terms even if the documents are judged relevant. In this paper our focus is to capture the limitation of PRF based query expansion and propose a hybrid method to improve the performance of PRF based query expansion by combining corpus based term co-occurrence information and semantic information of term. Firstly, the paper suggest use of corpus based term co-occurrence approach to select an optimal combination of query terms from a pool of terms obtained using PRF based query expansion. Second, we use semantic similarity approach to rank the query expansion terms obtained from top feedback documents. Third, we combine co-occurrence and semantic similarity together to rank the query expansion terms obtained from first step on the basis of semantic similarity. The experiments were performed on FIRE ad hoc and TREC-3 benchmark datasets of information retrieval. The results show significant improvement in terms of precision, recall and mean average precision (MAP). This experiments shows that the combination of both techniques in an intelligent way gives us goodness of both of them. As this is the first attempt in this direction there is a large scope of improving these techniques.