Abstract

Searching online text collections can be both rewarding and frustrating. While valuable information can be found, typically many irrelevant documents are also retrieved and many relevant ones are missed. Terminology mismatches between the user's query and document contents are a main cause of retrieval failures. Expanding a user's query with related words can improve search performance, but finding and using related words is an open problem. This research uses corpus analysis techniques to automatically discover similar words directly from the contents of the untagged databases. Using these similarities, user queries are automatically expanded, resulting in conceptual retrieval rather than requiring exact word matches between queries and documents. This work has been extended to multi-database collections where each sub-database has a collection-specific similarity matrix associated with it. If the best matrix is selected, substantial search improvements are possible. However, automati...

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