Most Information Retrieval models compute the relevance score of a document for a given query by summing term weights specific to a document or a query. Heuristic approaches, like TF-IDF, or probabilistic models, like BM25, are used to specify how a term weight is computed. In this paper, we propose to leverage learning-to-rank principles to learn how to compute a term weight for a given document based on the term occurrence pattern.
In parallel, Zheng and Callan @cite_13 proposed a term-independent representation of query terms to define the weight of each term. The central idea of their work is to represent each term of the query as the difference between the term vector and the mean of the vectors representing the terms of the query thus capturing the semantic difference between the term and the query. Our research is direction is orthogonal, since we are interested by the document weight and not the query one, but the idea of finding a term-independent representation inspired our present work.
@cite_13
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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.