Relevance Models are well-known retrieval models and capable of producing competitive results. However, because they use query expansion they can be very slow. We address this slowness by incorporating two variants of locality sensitive hashing (LSH) into the query expansion process. Results on two document collections suggest that we can obtain large reductions in the amount of work, with a small reduction in effectiveness. Our approach is shown to be additive when pruning query terms.
@article{arxiv.1607.02641,
title = {Randomised Relevance Model},
author = {Dominik Wurzer and Miles Osborne and Victor Lavrenko},
journal= {arXiv preprint arXiv:1607.02641},
year = {2016}
}
Comments
Information Retrieval, Query Expansion, Locality Sensitive Hashing, Randomized Algorithm, Relevance Model