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      Arabic Language Text Classification Using Dependency Syntax-Based Feature Selection

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          Abstract

          We study the performance of Arabic text classification combining various techniques: (a) tfidf vs. dependency syntax, for feature selection and weighting; (b) class association rules vs. support vector machines, for classification. The Arabic text is used in two forms: rootified and lightly stemmed. The results we obtain show that lightly stemmed text leads to better performance than rootified text; that class association rules are better suited for small feature sets obtained by dependency syntax constraints; and, finally, that support vector machines are better suited for large feature sets based on morphological feature selection criteria.

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          Author and article information

          Journal
          17 October 2014
          Article
          1410.4863
          fb9ccbfb-468c-4e10-95d5-60d370aaba32

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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          10 pages, 4 figure, accepted at CITALA 2014 (http://www.citala.org/)
          cs.CL

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