Khalifi, H. and Elqadi, A. and Ghanou, Y. (2018) Support vector machines for a new hybrid information retrieval system. In: UNSPECIFIED.

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Information Retrieval systems are used to extract, from a large database, relevant information for users. When the type of data is text, the complex nature of the database makes the process of retrieving information more difficult. Generally, such processes reformulate queries according to associations among information items before the query session. In this latter, semantic relationships or other approaches such as machine learning techniques can be applied to select the appropriate results to return. This paper presents a formal model and a new search algorithm. The proposed algorithm is applied to find associations between information items, and then use them to structure search results. It incorporates a natural language preprocessing stage, a statistical representation of short documents and queries and a machine learning model to select relevant results. On a series of experiments through Yahoo dataset, the proposed hybrid information retrieval system returned significantly satisfying results. © 2018 The Authors. Published by Elsevier B.V.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Uncontrolled Keywords: Classification (of information); Information retrieval; Information retrieval systems; Intelligent computing; Learning algorithms; Natural language processing systems; Query processing; Semantics; Supervised learning; Support vector machines, Machine learning models; Machine learning techniques; Natural languages; Pre-processing stages; Semantic relationships; Statistical representations; Supervised classification; Unsupervised classification, Search engines
Subjects: Computer Science
Divisions: SCIENTIFIC PRODUCTION > Computer Science
Depositing User: Administrateur Eprints Administrateur Eprints
Last Modified: 31 Jan 2020 15:46

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