The synergistic application of CBR to IR |
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Authors: | Edwina L. Rissland Jody J. Daniels |
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Affiliation: | (1) Department of Computer Science, University of Massachusetts, 01003 Amherst, MA |
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Abstract: | In this paper we discuss a hybrid approach combining Case-Based Reasoning (CBR) and Information Retrieval (IR) for the retrieval of full-text documents. Our hybrid CBR-IR approach takes as input a standard symbolic representation of a problem case and retrieves texts of relevant cases from a document collection dramatically larger than the case base available to the CBR system. Our system works by first performing a standard HYPO-style CBR analysis and then using the texts associated with certain important classes of cases found in this analysis to seed a modified version of INQUERY's relevance feedback mechanism in order to generate a query composed of individual terms or pairs of terms. Our approach provides two benefits: it extends the reach of CBR (for retrieval purposes) to much larger corpora, and it enables the injection of knowledge-based techniques into traditional IR. We describe our CBR-IR approach and report on on-going experiments.This research was supported by NSF Grant no. EEC-9209623, State/Industry/University Cooperative Research on Intelligent Information Retrieval, Digital Equipment Corporation, and the National Center for Automated Information Research. |
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Keywords: | IR AI hybrid CBR-IR automatic query generation INQUERY HYPO-style CBR |
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