Query modification using genetic algorithms in vector space models
International Journal of Expert Systems
An algorithm for suffix stripping
Readings in information retrieval
Journal of the American Society for Information Science
Applying genetic algorithms to query optimization in document retrieval
Information Processing and Management: an International Journal
Information Retrieval
Modern Information Retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
A test of genetic algorithms in relevance feedback
Information Processing and Management: an International Journal
Multiple query evaluation based on an enhanced genetic algorithm
Information Processing and Management: an International Journal - Modelling vagueness and subjectivity in information access
Automatic Information Organization and Retrieval.
Automatic Information Organization and Retrieval.
The SMART Retrieval System—Experiments in Automatic Document Processing
The SMART Retrieval System—Experiments in Automatic Document Processing
Improving the learning of Boolean queries by means of a multiobjective IQBE evolutionary algorithm
Information Processing and Management: an International Journal
Exploiting Morphological Query Structure Using Genetic Optimisation
NLDB '08 Proceedings of the 13th international conference on Natural Language and Information Systems: Applications of Natural Language to Information Systems
Structure of morphologically expanded queries: A genetic algorithm approach
Data & Knowledge Engineering
Using feature construction to avoid large feature spaces in text classification
Proceedings of the 12th annual conference on Genetic and evolutionary computation
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Nowadays, searching information in the web or in any kind of document collection has become one of the most frequent activities. However, user queries can be formulated in a way that hinder the recovery of the requested information. The objective of automatic query transformation is to improve the quality of the recovered information. This paper describes a new genetic algorithm used to change the set of terms that compose a user query without user supervision, by complementing an expansion process based on the use of a morphological thesaurus. We apply a stemming process to obtain the stem of a word, for which the thesaurus provides its different forms. The set of candidate query terms is constructed by expanding each term in the original query with the terms morphologically related. The genetic algorithm is in charge of selecting the terms of the final query from the candidate term set. The selection process is based on the retrieval results obtained when searching with different combination of candidate terms. We have obtained encouraging results, improving the performance of a standard set of tests.