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SEARCHING FOR EXPLANATORY WEB PAGES USING AUTOMATIC QUERY EXPANSION
Author(s) -
Tauchi Manabu,
Ward Nigel
Publication year - 2007
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/j.1467-8640.2007.00291.x
Subject(s) - query expansion , information retrieval , computer science , web search query , web query classification , relevance feedback , relevance (law) , search engine , term (time) , encyclopedia , web page , metric (unit) , world wide web , image retrieval , artificial intelligence , operations management , physics , quantum mechanics , library science , political science , law , economics , image (mathematics)
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 top n documents 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.