Machine Learning
A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Improving the effectiveness of information retrieval with local context analysis
ACM Transactions on Information Systems (TOIS)
Improving retrieval feedback with multiple term-ranking function combination
ACM Transactions on Information Systems (TOIS)
The role of variance in term weighting for probabilistic information retrieval
Proceedings of the eleventh international conference on Information and knowledge management
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Combining the language model and inference network approaches to retrieval
Information Processing and Management: an International Journal - Special issue: Bayesian networks and information retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
A generative theory of relevance
A generative theory of relevance
Flexible pseudo-relevance feedback via selective sampling
ACM Transactions on Asian Language Information Processing (TALIP)
Using query-specific variance estimates to combine Bayesian classifiers
ICML '06 Proceedings of the 23rd international conference on Machine learning
Regularized estimation of mixture models for robust pseudo-relevance feedback
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
A high-performance semi-supervised learning method for text chunking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
A cluster-based resampling method for pseudo-relevance feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A rank-aggregation approach to searching for optimal query-specific clusters
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Query-drift prevention for robust query expansion
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Statistical Language Models for Information Retrieval A Critical Review
Foundations and Trends in Information Retrieval
A User Profiles Acquiring Approach Using Pseudo-Relevance Feedback
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Navigating in the Dark: Modeling Uncertainty in Ad Hoc Retrieval Using Multiple Relevance Models
ICTIR '09 Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval Theory
Reducing the risk of query expansion via robust constrained optimization
Proceedings of the 18th ACM conference on Information and knowledge management
Robust query-specific pseudo feedback document selection for query expansion
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Geometric representations for multiple documents
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Using statistical decision theory and relevance models for query-performance prediction
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
PageRank without hyperlinks: Structural reranking using links induced by language models
ACM Transactions on Information Systems (TOIS)
Promoting divergent terms in the estimation of relevance models
ICTIR'11 Proceedings of the Third international conference on Advances in information retrieval theory
Is document frequency important for PRF?
ICTIR'11 Proceedings of the Third international conference on Advances in information retrieval theory
Predicting document effectiveness in pseudo relevance feedback
Proceedings of the 20th ACM international conference on Information and knowledge management
A Survey of Automatic Query Expansion in Information Retrieval
ACM Computing Surveys (CSUR)
A cluster based pseudo feedback technique which exploits good and bad clusters
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
Predicting query performance via classification
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Improving retrievability with improved cluster-based pseudo-relevance feedback selection
Expert Systems with Applications: An International Journal
Measuring the variability in effectiveness of a retrieval system
IRFC'10 Proceedings of the First international Information Retrieval Facility conference on Adbances in Multidisciplinary Retrieval
Selecting expansion terms as a set via integer linear programming
Proceedings of the 21st ACM international conference on Information and knowledge management
Modeling reformulation using query distributions
ACM Transactions on Information Systems (TOIS)
Reducing the uncertainty in resource selection
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
An incremental approach to efficient pseudo-relevance feedback
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Relevance-based language modelling for recommender systems
Information Processing and Management: an International Journal
A deterministic resampling method using overlapping document clusters for pseudo-relevance feedback
Information Processing and Management: an International Journal
A Theoretical Analysis of Pseudo-Relevance Feedback Models
Proceedings of the 2013 Conference on the Theory of Information Retrieval
Effective and Robust Query-Based Stemming
ACM Transactions on Information Systems (TOIS)
Bias-variance analysis in estimating true query model for information retrieval
Information Processing and Management: an International Journal
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Existing pseudo-relevance feedback methods typically perform averaging over the top-retrieved documents, but ignore an important statistical dimension: the risk or variance associated with either the individual document models, or their combination. Treating the baseline feedback method as a black box, and the output feedback model as a random variable, we estimate a posterior distribution for the feed-back model by resampling a given query's top-retrieved documents, using the posterior mean or mode as the enhanced feedback model. We then perform model combination over several enhanced models, each based on a slightly modified query sampled from the original query. We find that resampling documents helps increase individual feedback model precision by removing noise terms, while sampling from the query improves robustness (worst-case performance) by emphasizing terms related to multiple query aspects. The result is a meta-feedback algorithm that is both more robust and more precise than the original strong baseline method.