SEARCHING FOR EXPLANATORY WEB PAGES USING AUTOMATIC QUERY EXPANSION
Manabu Tauchi, Nigel Ward · Computational Intelligence · 2007
When one tries to use the Web as a dictionary or encyclopedia, entering some single term into a search engine, the highly ranked pages in the result can include irrelevant or useless sites. The problem is that single‐term queries, if taken literally, underspecify the type of page the user wants. For such problems automatic query expansion, also known as pseudo‐feedback, is often effective. In this method the topndocuments returned by an initial retrieval are used to provide terms for a second retrieval. This paper contributes, first, new normalization techniques for query expansion, and second, a new way of computing the similarity between an expanded query and a document, the “local relevance density” metric, which complements the standard vector product metric. Both of these techniques are shown to be useful for single‐term queries, in Japanese, in experiments done over the World Wide Web in early 2001.