Abstract
A new method for query expansion using genetic programming (GP) is proposed in this paper to enhance the retrieval performance of text information retrieval systems. Using a set of queries and retrieved relevant and non-relevant documents corresponding to each query, GP tries to evolve a criteria for selecting terms which when added to the original query improve the next retrieved set of documents. Two experiments are conducted to evaluate the proposed method over three standard datasets: Cranfield, Lisa and Medline. In first experiment a formula is evolved using GP over a training set and is then evaluated over a test query set of the same dataset. In the second experiment, evolved expansion formula over a dataset is evaluated over a different dataset. We compared our method against the base probabilistic method in literature. Results show a higher performance in comparison with original and probabilistically expanded method.
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Borji, A., Jahromi, M.Z. (2008). Evolving Weighting Functions for Query Expansion Based on Relevance Feedback. In: Zhang, Y., Yu, G., Bertino, E., Xu, G. (eds) Progress in WWW Research and Development. APWeb 2008. Lecture Notes in Computer Science, vol 4976. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78849-2_25
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DOI: https://doi.org/10.1007/978-3-540-78849-2_25
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