|Title||AUTOMATIC QUERY REWEIGHTING USING CO-OCCURRENCE GRAPHS|
|Publication Type||Conference Paper|
|Year of Publication||2019|
|Authors||Aklouche, B, Bounhas, I, Slimani, Y|
|Conference Name||16th International Conference on Applied Computing, Cagliari, Italy, November 7-9, 2019|
|Keywords||Ad-Hoc Information Retrieval, BM25, Co-occurrence graph, Query Reformulation, Query Reweighting, Term’s Discriminative Power|
Providing a relevant response to the user has always been challenging. Query reformulation methods have been widely applied in an attempt to provide a better representation of the user’s query and thus improve retrieval performance. In this paper, we present a new query reweighting method for document retrieval based on term co-occurrence graphs, which are built using a context window-based approach over the entire corpus. We propose an adapted version of the well-established Okapi BM25 model that allows identifying the most informative terms in the query and assigning them optimal weights. This measure stands out by its ability to evaluate the discriminative power of terms from co-occurrence graphs. Experimental results on two standard ad-hoc TREC collections show that our method improves both retrieval effectiveness and robustness and outperforms the state-of-the-art baselines with significant margins.