Estimating Query Performance using Class Predictions - Collins-Thompson, Kevyn; Bennett, Paul (Poster-Paper in Proceedings of the 32nd Annual ACM SIGIR Conference (SIGIR 2009), 2009)
We investigate using topic prediction data, as a summary of document content, to compute measures of search result quality. Unlike existing quality measures such as query clarity that require the entire content of the top-ranked results, class-based statistics can be computed eﬃciently online, because class information is compact enough to precompute and store in the index. In an empirical study we compare the performance of class-based statistics to their language model counterparts for predicting two measures: query difﬁculty and expansion risk. Our ﬁndings suggest that using class predictions can oﬀer comparable performance to full language models while reducing computation overhead.