Exploiting clustering and phrases for context-based information retrieval

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 used to illustrate the advantages of such a context-based retrieval paradigm.