Abstract
Nowadays, searching the relevant documents from a large dataset becomes a big challenge. Automatic query expansion is one of the techniques, which addresses this problem by refining the query. A new query expansion approach using cuckoo search and accelerated particle swarm optimization technique is proposed in this paper. The proposed approach mainly focused to find the most relevant expanded query rather than suitable expansion terms. In this paper, Fuzzy logic is also employed, which improves the performance of accelerated particle swarm optimization by controlling various parameters. We have compared the proposed approach with other existing and recently developed automatic query expansion approaches on various evaluating parameters such as average recall, average precision, Mean-Average Precision, F-measure and precision-recall graph. We have evaluated the performance of all approaches on three datasets CISI, CACM and TREC-3. The results obtained for all three datasets depict that the proposed approach gets better results in comparison to other automatic query expansion approaches.
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Acknowledgements
We are very thankful to anonymous reviewers for their valuable suggestions. We are also thankful to Dr. Ashish Saini and Dr. Yogesh Gupta to provide their support to access datasets for experiments.
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Sharma, D.K., Pamula, R. & Chauhan, D.S. A hybrid evolutionary algorithm based automatic query expansion for enhancing document retrieval system. J Ambient Intell Human Comput 15, 829–848 (2024). https://doi.org/10.1007/s12652-019-01247-9
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DOI: https://doi.org/10.1007/s12652-019-01247-9