Selection of Passages for Information Reduction

Abstract

is a huge manual undertaking, particularly when there are fifty or more texts. Unfortunately, full-text understanding is not yet feasible as an alternative and information extract techniques themselves rely on large numbers of training texts with manually encoded answer keys. By locating and presenting relevant passages to the user, we will have significantly reduced the time and effort expenditure. Alternatively, we could save an automated informationextraction system from processing an entire text by focusing the system on those portions of the text most likely to contain the desired information. This work integrates a case-based reasoner with an IR engine to reduce the information bottleneck. SPIRE [Se- This research was supported byNSF Grant no. EEC-9209623, State/Industry/University Cooperative Research on Intelligent Information Retrieval, Digital Equipment Corporation and the National Center for Automated Information Research. lection of Passages for Inf