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This paper presents an approach based on supervised machine learning methodsto build a classifier that can identify text complexity in order to presentArabic language learners with texts suitable to their levels. The approach isbased on machine learning classification methods to discriminate between thedifferent levels of difficulty in reading and understanding a text. Severalmodels were trained on a large corpus mined from online Arabic websites andmanually annotated. The model uses both Count and TF-IDF representations andapplies five machine learning algorithms; Multinomial Naive Bayes, BernoulliNaive Bayes, Logistic Regression, Support Vector Machine and Random Forest,using unigrams and bigrams features. With the goal of extracting the textcomplexity, the problem is usually addressed by formulating the levelidentification as a classification task. Experimental results showed thatn-gram features could be indicative of the reading level of a text and couldsubstantially improve performance, and showed that SVM and Multinomial NaiveBayes are the most accurate in predicting the complexity level. Best resultswere achieved using TF-IDF Vectors trained by a combination of word-basedunigrams and bigrams with an overall accuracy of 87.14% over four classes ofcomplexity.

Efficient Measuring of Readability to Improve Documents Accessibility for Arabic Language Learners