Local feedback and intelligent automatic query expansion
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TL;DR
An iterative method for information retrieval uses searchonyms found from the previously retrieved set of documents in query expansion to form the feedback seed, which is subsequently used in query reformulation.
Abstract
An iterative method for information retrieval is presented. It uses searchonyms found from the previously retrieved set of documents in query expansion. Only largest values of relation of resemblance between the query and the documents are used to form the feedback seed. From this top retrieved set of documents, most informative features are selected as searchonyms, which are subsequently used in query reformulation. Large operational bibliographic data bases are used to simulate the behavior of this method.