El Ghali, B. and El Qadi, A. and Ouadou, M. and Aboutajdine, D. (2015) Context-based query expansion method for short queries using latent semantic analyses. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9466. pp. 468-473.

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Short queries are the key difficulty in information retrieval (IR). A plenty of query expansion techniques has been proposed to solve this problem. In this paper, we propose three different models for query suggestion using the cosine similarity (CS), the Language Models (LM) or their fusion. The expansion terms are selected using the Latent Semantic Analyses method based on the result of the three query suggestion methods. The approaches proposed improve the precision of the user query by adding additional context to it. Experimental results show that expanding short queries by our approaches improves the effectiveness of the IR system by 48,1 using the CS based model, 19,2 using the LM model, and 13,5 using the fusion model. © Springer International Publishing Switzerland 2015.

Item Type: Article
Uncontrolled Keywords: Artificial intelligence; Computer science; Computers, Context-based; Cosine similarity; Language model; Latent Semantic Analysis; Query context; Query expansion; Query expansion techniques; Query suggestion, Semantics
Subjects: Computer Science
Divisions: SCIENTIFIC PRODUCTION > Computer Science
Depositing User: Administrateur Eprints Administrateur Eprints
Last Modified: 31 Jan 2020 15:46
URI: http://eprints.umi.ac.ma/id/eprint/2660

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