Method of Lexical Enrichment in Information Retrieval System in Arabic

Souheyl Mallat, Anis Zouaghi, Emna Hkiri and Mounir Zrigui
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Souheyl Mallat: Department of Computer Sciences, University of Monastir, Monastir, Tunisia
Anis Zouaghi: Department of Computer Sciences, Higher Institute of Applied Science and Technologies Sousse, Sousse University, Sousse, Tunisia
Emna Hkiri: Department of Computer Sciences, University of Monastir, Monastir, Tunisia
Mounir Zrigui: Department of Computer Sciences, University of Monastir, Monastir, Tunisia

International Journal of Information Retrieval Research (IJIRR), 2013, vol. 3, issue 4, 35-51

Abstract: In this paper, the authors propose a method for lexical enrichment of Arabic queries in order to improve the performance of the information retrieval systems SRI. This method has two types of enrichment: linguistic and contextual. The first one is based on the linguistic analysis (lemmatization, morphological, syntactic and semantic analysis), whose goal is to generate a descriptive list (list-desc). This list contains a set of linguistic lexicon assigned to each significant term in the query. The second enrichment consists in integrating contextual information derived from the corpus documents. It is based on statistical analysis using Salton weighting functions: TF-IDF and TF-IEF. The TF-IDF function is applied on the list-desc and documents in the corpus in order to identify relevant documents. TF-IEF function is made between the list-desc and sentences belonging to the relevant documents to identify relevant sentences. Then, terms in these sentences are weighted, and those with highest weights are considered rich in terms of informative and contextual importance are added to the original query. The authors' lexical enrichment method was evaluated on a corpus of documents belonging to a specialized domain and results show its interest in terms of precision and recall.

Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jirr00:v:3:y:2013:i:4:p:35-51

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