Abstract

Accessing online information remains an inexact science. 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 is a main cause of retrieval failures. Expanding a user's query with related words can improve search performance, but the problem of identifying related words remains. This research uses corpus linguistics techniques to automatically discover word similarities directly from the contents of the untagged TREC database and to incorporates that information in the SMART information retrieval system. The similarities are calculated based on the contexts in which a set of target words appear. Using these similarities, user queries are automatically expanded, resulting in conceptual retrieval rather than requiring exact word matches between queries and documents. 1. INTRODUCTION Expanding a user's query with related terms can improve sea...

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