@inproceedings{collins-thompson2009estimating,
author = {Collins-Thompson, Kevyn and Bennett, Paul},
title = {Estimating Query Performance using Class Predictions},
booktitle = {Poster-Paper in Proceedings of the 32nd Annual ACM SIGIR Conference (SIGIR 2009)},
year = {2009},
month = {January},
abstract = {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.},
publisher = {ACM},
url = {http://approjects.co.za/?big=en-us/research/publication/estimating-query-performance-using-class-predictions/},
edition = {Poster-Paper in Proceedings of the 32nd Annual ACM SIGIR Conference (SIGIR 2009)},
}