Latent Topic Model for Indexing Arabic Documents

Rami Ayadi, Mohsen Maraoui and Mounir Zrigui
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Rami Ayadi: LaTice Lab, Faculty of Economics and Management of Sfax, University of Sfax, Sfax, Tunisia
Mohsen Maraoui: LaTice Lab, Faculty of Sciences of Monastir, University of Monastir, Monastir, Tunisia
Mounir Zrigui: LaTice Lab, Faculty of Sciences of Monastir, University of Monastir, Monastir, Tunisia

International Journal of Information Retrieval Research (IJIRR), 2014, vol. 4, issue 1, 29-45

Abstract: In this paper, the authors present latent topic model to index and represent the Arabic text documents reflecting more semantics. Text representation in a language with high inflectional morphology such as Arabic is not a trivial task and requires some special treatments. The authors describe our approach for analyzing and preprocessing Arabic text then we describe the stemming process. Finally, the latent model (LDA) is adapted to extract Arabic latent topics, the authors extracted significant topics of all texts, each theme is described by a particular distribution of descriptors then each text is represented on the vectors of these topics. The experiment of classification is conducted on in house corpus; latent topics are learned with LDA for different topic numbers K (25, 50, 75, and 100) then the authors compare this result with classification in the full words space. The results show that performances, in terms of precision, recall and f-measure, of classification in the reduced topics space outperform classification in full words space and when using LSI reduction.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jirr00:v:4:y:2014:i:1:p:29-45

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