Although many retrieval models incorporating term dependency have been developed, it is still unclear whether term dependency information can consistently enhance retrieval performance for different queries. We present a novel model that captures the main components of a topic and the relationship between those components and the power of term dependency to improve retrieval performance. Experimental results demonstrate that the power of term dependency strongly depends on the relationship between these components. Without relevance information, the model is still useful by predicting the components based on global statistical information. We show the applicability of the model for adaptively incorporating term dependency for individual queries.
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