The majority of the current information retrieval models weight the query concepts (e.g., terms or phrases) in an unsupervised manner, based solely on the collection statistics. In this paper, we go beyond the unsupervised estimation of concept weights, and propose a parameterized concept weighting model. In our model, the weight of each query concept is determined using a parameterized combination of diverse importance features. Unlike the existing supervised ranking methods, our model learns importance weights not only for the explicit query concepts, but also for the latent concepts that are associated with the query through pseudo-relevance feedback. The experimental results on both newswire and web TREC corpora show that our model consistently and significantly outperforms a wide range of state-of-the-art retrieval models. In addition, our model significantly reduces the number of latent concepts used for query expansion compared to the nonparameterized pseudo-relevance feedback based models.
Parameterized query expansion method REF ] takes a somewhat similar approach where the weight of each query concept is determined using a parameterized combination of diverse importance features.
Parameterized concept weighting in verbose queries
SIGIR '11
Computer Science