Building searchable collections of enterprise speech data
Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries
SESAM: searching supported by analysis of metadata
Proceedings of the 2002 ACM symposium on Applied computing
Detecting similar documents using salient terms
Proceedings of the eleventh international conference on Information and knowledge management
An Agent Framework for Intranet Document Management
Autonomous Agents and Multi-Agent Systems
Mining anchor text for query refinement
Proceedings of the 13th international conference on World Wide Web
Synchronized tag clouds for exploring semi-structured clinical trial data
CASCON '08 Proceedings of the 2008 conference of the center for advanced studies on collaborative research: meeting of minds
Methods and algorithms for automatic text analysis
Automatic Documentation and Mathematical Linguistics
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Typically, users submit very simple search queries to digital document data collections. Often these queries can result in extremely broad answers or answers in which document relevance is hard to assess. Our group has developed a suite of tools which build sophisticated indexes to document collections. These tools can be used to provide cues to help users formulate more effective queries. In order to present these cues and manage the process of query refinement, we have developed a Java-based client server system which uses these indexes and tools. One of the most powerful parts of our system is its ability to recognize domain-specific multi-word names and terms, using heuristic methods, and to index these vocabulary items in such a way that we can look up vocabulary items that commonly are related to the original query terms. This system responds to an initial query by suggesting additional items that the user can use to focus the query.