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• W2397390177 abstract "Developments in Arabic information retrieval did not follow the increasing use of the Arabic Web during the last decade. Semantic indexing in a language with high inflectional morphology, such as Arabic, is not a trivial task and requires a text analysis in the original language. Excepting cross-language retrieval methods or limited studies, the main efforts, for developing semantic analysis methods and topic modeling, did not include Arabic text. This paper describes our approach for analyzing semantics in Arabic texts. A new lemma-based stemmer is developed and compared to root-based one for characterizing Arabic text. The Latent Dirichlet Allocation (LDA) model is adapted to extract Arabic latent topics from various real-world corpora. In addition to the interesting subjects discovered in the press articles during the 2007-2009 period, experiments show that the classification performances with lemma-based stemming in the topics space, are improved when comparing to classification with root-based stemming." @default.
• W2397390177 created "2016-06-24" @default.
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• W2397390177 date "2013-01-01" @default.
• W2397390177 modified "2023-09-26" @default.
• W2397390177 title "An arabic lemma-based stemmer for latent topic modeling." @default.
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• W2397390177 hasPublicationYear "2013" @default.
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