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Hybrid Query Expansion Model Based on Pseudo Relevance Feedback and Semantic Tree for Arabic IR

Hybrid Query Expansion Model Based on Pseudo Relevance Feedback and Semantic Tree for Arabic IR

Ahmed Cherif Mazari, Abdelhamid Djeffal
Copyright: © 2022 |Volume: 12 |Issue: 1 |Pages: 16
ISSN: 2155-6377|EISSN: 2155-6385|EISBN13: 9781683182085|DOI: 10.4018/IJIRR.289949
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MLA

Mazari, Ahmed Cherif, and Abdelhamid Djeffal. "Hybrid Query Expansion Model Based on Pseudo Relevance Feedback and Semantic Tree for Arabic IR." IJIRR vol.12, no.1 2022: pp.1-16. http://doi.org/10.4018/IJIRR.289949

APA

Mazari, A. C. & Djeffal, A. (2022). Hybrid Query Expansion Model Based on Pseudo Relevance Feedback and Semantic Tree for Arabic IR. International Journal of Information Retrieval Research (IJIRR), 12(1), 1-16. http://doi.org/10.4018/IJIRR.289949

Chicago

Mazari, Ahmed Cherif, and Abdelhamid Djeffal. "Hybrid Query Expansion Model Based on Pseudo Relevance Feedback and Semantic Tree for Arabic IR," International Journal of Information Retrieval Research (IJIRR) 12, no.1: 1-16. http://doi.org/10.4018/IJIRR.289949

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Abstract

In this paper, the authors propose and readapt a new concept-based approach of query expansion in the context of Arabic information retrieval. The purpose is to represent the query by a set of weighted concepts in order to identify better the user's information need. Firstly, concepts are extracted from the initially retrieved documents by the Pseudo-Relevance Feedback method, and then they are integrated into a semantic weighted tree in order to detect more information contained in the related concepts connected by semantic relations to the primary concepts. The authors use the “Arabic WordNet” as a resource to extract, disambiguate concepts and build the semantic tree. Experimental results demonstrate that measure of MAP (Mean Average Precision) is about 10% of improvement using the open source Lucene as IR System on a collection formed from the Arabic BBC news.