Abstract
We present a new learning to rank framework for estimating context-sensitive term weights without use of feedback. Specifically, knowledge of effective term weights on past queries is used to estimate term weights for new queries. This generalization is achieved by introducing secondary features correlated with term weights and applying regression to predict term weights given features. To improve support for more focused retrieval like question answering, we conduct document retrieval experiments with TREC description queries on three document collections. Results show significantly improved retrieval accuracy.
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Lease, M., Allan, J., Croft, W.B. (2009). Regression Rank: Learning to Meet the Opportunity of Descriptive Queries. In: Boughanem, M., Berrut, C., Mothe, J., Soule-Dupuy, C. (eds) Advances in Information Retrieval. ECIR 2009. Lecture Notes in Computer Science, vol 5478. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00958-7_11
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DOI: https://doi.org/10.1007/978-3-642-00958-7_11
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