On term selection for query expansion
Journal of Documentation
Document language models, query models, and risk minimization for information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Model-based feedback in the language modeling approach to information retrieval
Proceedings of the tenth international conference on Information and knowledge management
Towards More Effective Techniques for Automatic Query Expansion
ECDL '99 Proceedings of the Third European Conference on Research and Advanced Technology for Digital Libraries
Spectral Partitioning Works: Planar Graphs and Finite Element Meshes
Spectral Partitioning Works: Planar Graphs and Finite Element Meshes
A study of smoothing methods for language models applied to information retrieval
ACM Transactions on Information Systems (TOIS)
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
Selecting good expansion terms for pseudo-relevance feedback
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 unified optimization framework for robust pseudo-relevance feedback algorithms
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Interactive sense feedback for difficult queries
Proceedings of the 20th ACM international conference on Information and knowledge management
Selecting expansion terms as a set via integer linear programming
Proceedings of the 21st ACM international conference on Information and knowledge management
A document is known by the company it keeps: neighborhood consensus for short text categorization
Language Resources and Evaluation
Selecting effective expansion terms for diversity
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
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It is well known that pseudo-relevance feedback (PRF) improves the retrieval performance of Information Retrieval (IR) systems in general. However, a recent study by Cao et al [3] has shown that a non-negligible fraction of expansion terms used by PRF algorithms are harmful to the retrieval. In other words, a PRF algorithm would be better off if it were to use only a subset of the feedback terms. The challenge then is to find a good expansion set from the set of all candidate expansion terms. A natural approach to solve the problem is to make term independence assumption and use one or more term selection criteria or a statistical classifier to identify good expansion terms independent of each other. In this work, we challenge this approach and show empirically that a feedback term is neither good nor bad in itself in general; the behavior of a term depends very much on other expansion terms. Our finding implies that a good expansion set can not be found by making term independence assumption in general. As a principled solution to the problem, we propose spectral partitioning of expansion terms using a specific term-term interaction matrix. We demonstrate on several test collections that expansion terms can be partitioned into two sets and the best of the two sets gives substantial improvements in retrieval performance over model-based feedback.