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Journals ACM SIGIR Forum Volume 31: Special Issue

Exploiting Clustering and Phrases for Context-Based Information Retrieval Peter G. Anick Digital Equipment Corporation, Stow, MA. and Brandeis University, Waltham, MA. Anick@mail.dec.com and Shivakumar Vaithyanathan IBM Almaden Research Center, San Jose, CA. Shiv@almaden.ibm.com Abstract This paper explores exploiting the synergy between document clustering and phrasal analysis for the purpose of automatically constructing a corrrex~-busedretrieval system. A contex~ consists of two components - a cluster of logically related articles (its exrension) and a small set of salient concepts, represented by words and phrases and organized by the cluster ™s key terms (its irr~ertsion). At inn-time, the system presents contexts that best match the result list of a user ™s natural language query. The user can then choose a context and manipulate the intensionsd component to both browse the context ™s extension and launch new searches over the entire database. We argue that the focused relevance feedback provided by contexts, at a level of abstraction higher than individual documents and lower than the database as a whole, provides a natural way for users to refine vague information needs and helps to blur the distinction between searching and browsing. The I%zraphrase interface, running over a database of business-related news articles, is

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Exploiting clustering and phrases for context-based information retrieval

Anick, Peter G.; Vaithyanathan, Shivakumar
ACM SIGIR Forum , Volume 31 (SI)
Association for Computing Machinery – Dec 2, 1997

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