On modeling of information retrieval concepts in vector spaces
ACM Transactions on Database Systems (TODS)
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
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
An information-theoretic approach to automatic query expansion
ACM Transactions on Information Systems (TOIS)
European Research Letter: cross-language system evaluation: the CLEF campaigns
Journal of the American Society for Information Science and Technology
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Modern Information Retrieval
A test of genetic algorithms in relevance feedback
Information Processing and Management: an International Journal
Dependence Among Terms in Vector Space Model
IDEAS '04 Proceedings of the International Database Engineering and Applications Symposium
Set-based vector model: An efficient approach for correlation-based ranking
ACM Transactions on Information Systems (TOIS)
Query reformulation using automatically generated query concepts from a document space
Information Processing and Management: an International Journal
Improving query expansion with stemming terms: a new genetic algorithm approach
EvoCOP'08 Proceedings of the 8th European conference on Evolutionary computation in combinatorial optimization
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In this paper we deal with two issues. First, we discuss the negative effects of term correlation in query expansion algorithms, and we propose a novel and simple method (query clauses) to represent expanded queries which may alleviate some of these negative effects. Second, we discuss a method to optimise local query expansion methods using genetic algorithms, and we apply this method to improve stemming. We evaluate this method with the novel query representation method and show very significant improvements for the problem of optimising stemming.