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Using Induction to Refine Information Retrieval StrategiesConceptual information retrieval systems use structured document indices, domain knowledge and a set of heuristic retrieval strategies to match user queries with a set of indices describing the document's content. Such retrieval strategies increase the set of relevant documents retrieved (increase recall), but at the expense of returning additional irrelevant documents (decrease precision). Usually in conceptual information retrieval systems this tradeoff is managed by hand and with difficulty. This paper discusses ways of managing this tradeoff by the application of standard induction algorithms to refine the retrieval strategies in an engineering design domain. We gathered examples of query/retrieval pairs during the system's operation using feedback from a user on the retrieved information. We then fed these examples to the induction algorithm and generated decision trees that refine the existing set of retrieval strategies. We found that (1) induction improved the precision on a set of queries generated by another user, without a significant loss in recall, and (2) in an interactive mode, the decision trees pointed out flaws in the retrieval and indexing knowledge and suggested ways to refine the retrieval strategies.
Document ID
19990111711
Acquisition Source
Ames Research Center
Document Type
Reprint (Version printed in journal)
Authors
Baudin, Catherine
(RECOM Software, Inc. Moffett Field, CA United States)
Pell, Barney
(Research Inst. for Advanced Computer Science Moffett Field, CA United States)
Kedar, Smadar
(Northwestern Univ. Evanston, IL United States)
Date Acquired
August 19, 2013
Publication Date
June 1, 1994
Subject Category
Documentation And Information Science
Report/Patent Number
TR-FIA-94-08
Distribution Limits
Public
Copyright
Other

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