Relevance feedback with too much data

1 January 1995

conference paper
Published by Association for Computing Machinery (ACM)

p. 337-343
https://doi.org/10.1145/215206.215380

Abstract

Relevance Feedbacvk With Too Much Data A UMass Technical Report James Allan IR-57 (ref.TR95-6) Modern text collections often contain large documents which span several subject areas. Such documents are problematic for relevance feedback since inappropriate terms can easily be chosen. This study explores the highly effective approach of feeding back passages of large documents. A less- expensive method which discards long documents is also reviewed and found to be effective if there are enough relevant documents. A hybrid approach which feeds back short documents and passages of long documents may be the best compromise.

Keywords

EFFECTIVE APPROACH
RELEVANCE FEEDBACVK
LONG DOCUMENT
RELEVANCE FEEDBACK
INAPPROPRIATE TERM
HYBRID APPROACH
LARGE DOCUMENT
BEST COMPROMISE
DATA A UMASS TECHNICAL
EXPENSIVE METHOD
REPORT JAMES ALLAN IR-57
INDIVIDUAL DIFFERENCES
INFORMATION RETRIEVAL
QUERY LANGUAGES

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