<p>Query expansion aims to address the term mismatch problem by adding relevant terms to the original query. Traditional approaches, particularly those based on relevance feedback, have shown significant improvements in information retrieval, but rely mainly on lexical information for selecting expansion terms. In contrast, contextual models such as BERT have proven effective in capturing semantic relationships between queries and documents, leading to improved retrieval performance. In this work, we propose an extension of the classical relevance model that incorporates BERT-based contextual information into query model estimation. Specifically, our approach leverages passage-level signals to better estimate the importance of expansion terms. The resulting model is combined with the original relevance model to form a hybrid query model for document retrieval. Experiments on four TREC collections demonstrate that our approach outperforms the baseline relevance model and traditional query expansion methods. It also surpasses two BERT-based query expansion models, CEQE and SRoc, across most evaluation metrics, while achieving competitive performance compared to the dense retrieval model ColBERT. These results highlight the effectiveness of the proposed contextualized hybrid expansion approach.</p>