The cluster hypothesis states: closely related documents tend to be relevant to the same request. We exploit this hypothesis directly by adjusting ad hoc 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 semisupervised learning. We demonstrate that regularized scores consistently and significantly rank documents better than un-regularized scores, given a variety of initial retrieval algorithms. We evaluate our method on two large corpora across a substantial number of topics.
A local score regularization method REF uses a document affinity matrix to adjust initial retrieval scores so that topically related documents receive similar scores.
Regularizing ad hoc retrieval scores
CIKM '05
Computer Science