Predicting query performance in microblog retrieval

Rodriguez Perez, J. A. and Jose, J. M. (2014) Predicting query performance in microblog retrieval. In: 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, Gold Coast, Qld, Australia, 6-11 Jul 2014, pp. 1183-1186. ISBN 9781450322577 (doi:10.1145/2600428.2609540)

Full text not currently available from Enlighten.


Query Performance Prediction (QPP) is the estimation of the retrieval success for a query, without explicit knowledge about relevant documents. QPP is especially interesting in the context of Automatic Query Expansion (AQE) based on Pseudo Relevance Feedback (PRF). PRF-based AQE is known to produce unreliable results when the initial set of retrieved documents is poor. Theoretically, a good predictor would allow to selectively apply PRF-based AQE when performance of the initial result set is good enough, thus enhancing the overall robustness of the system. QPP would be of great benefit in the context of microblog retrieval, as AQE was the most widely deployed technique for enhancing retrieval performance at TREC. In this work we study the performance of the state of the art predictors under microblog retrieval conditions as well as introducing our own predictors. Our results show how our proposed predictors outperform the baselines significantly.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Jose, Professor Joemon and Rodriguez Perez, Mr Jesus
Authors: Rodriguez Perez, J. A., and Jose, J. M.
College/School:College of Science and Engineering > School of Computing Science

University Staff: Request a correction | Enlighten Editors: Update this record

Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
572791LiMoSINe: Linguistically Motivated Semantic aggregatIon eNginesJoemon JoseEuropean Commission (EC)288024COM - COMPUTING SCIENCE