Update: 30 September, 2023
This paper studies the parameter sensitivity of term frequency normalization in the probabilistic model for information retrieval. High parameter sensitivity indicates that a slight change of the parameter value may considerably affect the retrieval performance. Therefore, a weighting model with a high parameter sensitivity is not robust enough to provide a consistent retrieval performance across different collections and queries. We suggest that the parameter sensitivity is due to the fact that the query term weights are not adequate enough to allow informative query terms to differ from non-informative ones. We show that query term reweighing, which is part of the relevance feedback process, can be successfully used to reduce the parameter sensitivity. Experiments on five Text REtrieval Conference (TREC) collections show that the parameter sensitivity does remarkably decrease when query terms are reweighed.
File format: PDF
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MD5 Checksum: 149BDBED97B04D1F0C89F9F488CF4CBB
Publication date: 25 April, 2012
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PDF Link: Parameter Sensitivity in the Probabilistic Model for Ad-hoc Retrieval Manual PDF