Learning to Reweight Terms with Distributed Representations

Term weighting is a fundamental problem in IR research and numerous weighting models have been proposed. Proper term weighting can greatly improve retrieval accuracies, which essentially involves two types of query understanding: interpreting the query and judging the relative contribution of the terms to the query. These two steps are often dealt with separately, and complicated yet not so effective weighting strategies are proposed. In this paper, we propose to address query interpretation and term weighting in a unified framework built upon distributed representations of words from recent advances in neural network language modeling. Specifically, we represent term and query as vectors in the same latent space, construct features for terms using their word vectors and learn a model to map the features onto the defined target term weights. The proposed method is simple yet effective. Experiments using four collections and two retrieval models demonstrates significantly higher retrieval accuracies than baseline models.