Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Journal of the American Society for Information Science
Crossover improvement for the genetic algorithm in information retrieval
Information Processing and Management: an International Journal
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Topic prediction based on comparative retrieval rankings
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
A generic ranking function discovery framework by genetic programming for information retrieval
Information Processing and Management: an International Journal
Journal of the American Society for Information Science and Technology
Query association surrogates for Web search: Research Articles
Journal of the American Society for Information Science and Technology
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Predicting query difficulty on the web by learning visual clues
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Proposal of two-stage patent retrieval method considering the claim structure
ACM Transactions on Asian Language Information Processing (TALIP)
Evolving General Term-Weighting Schemes for Information Retrieval: Tests on Larger Collections
Artificial Intelligence Review
Patent claim processing for readability: structure analysis and term explanation
PATENT '03 Proceedings of the ACL-2003 workshop on Patent corpus processing - Volume 20
On ranking the effectiveness of searches
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Ranking robustness: a novel framework to predict query performance
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Information Systems
Introduction to the special issue on patent processing
Information Processing and Management: an International Journal
Web projections: learning from contextual subgraphs of the web
Proceedings of the 16th international conference on World Wide Web
Genetic Programming-Based Discovery of Ranking Functions for Effective Web Search
Journal of Management Information Systems
Query performance prediction in web search environments
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Performance prediction using spatial autocorrelation
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Swarming to rank for information retrieval
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Estimating query performance using class predictions
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Extended gloss overlaps as a measure of semantic relatedness
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Query hardness estimation using Jensen-Shannon divergence among multiple scoring functions
ECIR'07 Proceedings of the 29th European conference on IR research
Effective pre-retrieval query performance prediction using similarity and variability evidence
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Using coherence-based measures to predict query difficulty
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Learning to rank for why-question answering
Information Retrieval
An analysis on topic features and difficulties based on web navigational retrieval experiments
AIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology
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Predicting the effectiveness of queries plays an important role in information retrieval. In recent years, a number of methods are proposed for this task, however, there has been little work done on combining multiple predictors. Previous studies on combining multiple predictors rely on non-backtracking based machine learning methods. These studies show minor improvement over single predictors due to the limitation of non-backtracking. This paper discusses work on using machine learning to automatically generate an effective predictors' combination for query performance prediction. This task is referred to as--learning to predict for query performance prediction in the field. In this paper, a learning method, PredGP, is presented to address this task. PredGP employs genetic programming to learn a predictor by combining various pre-retrieval predictors. The proposed method is evaluated using the TREC Chemical Prior-Art Retrieval Task dataset and found to be significantly better than single predictors.