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A New Hybrid Document Clustering for PRF-Based Automatic Query Expansion Approach for Effective IR

A New Hybrid Document Clustering for PRF-Based Automatic Query Expansion Approach for Effective IR

Yogesh Gupta, Ashish Saini
Copyright: © 2020 |Volume: 16 |Issue: 3 |Pages: 23
ISSN: 1548-3673|EISSN: 1548-3681|EISBN13: 9781799805175|DOI: 10.4018/IJeC.2020070105
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MLA

Gupta, Yogesh, and Ashish Saini. "A New Hybrid Document Clustering for PRF-Based Automatic Query Expansion Approach for Effective IR." IJEC vol.16, no.3 2020: pp.73-95. https://doi.org/10.4018/IJeC.2020070105

APA

Gupta, Y. & Saini, A. (2020). A New Hybrid Document Clustering for PRF-Based Automatic Query Expansion Approach for Effective IR. International Journal of e-Collaboration (IJeC), 16(3), 73-95. https://doi.org/10.4018/IJeC.2020070105

Chicago

Gupta, Yogesh, and Ashish Saini. "A New Hybrid Document Clustering for PRF-Based Automatic Query Expansion Approach for Effective IR," International Journal of e-Collaboration (IJeC) 16, no.3: 73-95. https://doi.org/10.4018/IJeC.2020070105

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Abstract

Automatic query expansion (AQE) is an effective measure to improve information retrieval performance by including additional terms in a user query. The pseudo relevance feedback (PRF) method employed for AQE so far has suffered from a major problem of query drift. Therefore, keeping it in view, a new hybrid document clustering for PRF based AQE approach is proposed in the present article. In this, Fuzzy logic and Particle Swarm Optimization (PSO) are used to construct document clusters. Further, a new and effective hybrid PSO and Fuzzy logic-based term weighting approach is followed to find more suitable additional query terms using a weighted score of four IR evidences which is considered maximized. Moreover, a combined semantic filtering method along with query terms re-weighting algorithms are also used to remove noisy or irrelevant terms semantically. The performance of the presented approaches in this article is tested and compared with other approaches on three benchmark data sets. The comparative analysis of all the tested approaches illustrates the superior performance of the proposed approach.

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