Estimating Robust Query Models with Convex Optimization

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Estimating Robust Query Models with Convex Optimization
Query expansion is a long-studied approach for improving retrieval effectiveness by enhancing the user's original query with additional related words. Current algorithms for automatic query expansion can often improve retrieval accuracy on average, but are not robust: that is, they are highly unstable and have poor worst-case performance for individual queries. To address this problem, we introduce a novel formulation of query expansion as a convex optimization problem over a word graph. The model combines initial weights from a baseline feedback algorithm with edge weights based on word similarity, and integrates simple constraints to enforce set-based criteria such as aspect balance, aspect coverage, and term centrality. Results across multiple standard test collections show consistent and significant reductions in the number and magnitude of expansion failures, while retaining the strong positive gains of the baseline algorithm. Our approach does not assume a particular retrie...
Kevyn Collins-Thompson
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where NIPS
Authors Kevyn Collins-Thompson
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