Regularizing query-based retrieval scores

In information retrieval, the cluster hypothesis states: closely related documents tend to be relevant to the same request. We exploit this hypothesis directly by adjusting querybased information retrieval scores from an initial retrieval so that topically related documents receive similar scores. We refer to this process as score regularization. Score regularization can be presented as an optimization problem, allowing the use of results from semi-supervised learning. We demonstrate that regularized scores consistently and significantly rank documents better than unregularized scores, given a variety of initial retrieval algorithms.

• Type: Journal
• Year: 2007
• Where: IR
• Authors: Fernando Diaz
• Keywords: Information Retrieval, IR 2007, Natural Language Processing, Related Documents, Score Regularization