Arabic Language Text Classification Using Dependency Syntax-Based Feature Selection - Archive ouverte HAL
Communication Dans Un Congrès Année : 2014

Arabic Language Text Classification Using Dependency Syntax-Based Feature Selection

Résumé

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.

Dates et versions

hal-01185094 , version 1 (19-08-2015)

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Yannis Haralambous, Yassir Elidrissi, Philippe Lenca. Arabic Language Text Classification Using Dependency Syntax-Based Feature Selection. CITALA 2014 : 5e Conférence Internationale sur le Traitement Automatique de la Langue Arabe, Nov 2014, Oujda, Morocco. pp.31-40. ⟨hal-01185094⟩
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