Abstract
Researchers have recently shown that document scores of a number of different text search engines may be fitted on a per query basis using an exponential distribution for the set of non-relevant documents and a normal distribution for the set of relevant documents. This model fits a large number of different search engines including probabilistic search engines like INQUERY, vector space search engines like SMART and also LSI search engines and a language model engine. The model also appears to be true of search engines operating on a number of different languages. This leads to the hypothesis that all ‘good’ text search engines operating on any language have similar characteristics.
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