Efficient Measuring of Readability to Improve Documents Accessibility for Arabic Language Learners
This work addresses document accessibility for Arabic language learners by providing a tool to match texts to appropriate reading levels, though it is incremental as it applies standard machine learning methods to a specific language domain.
The paper tackled the problem of automatically identifying text complexity levels for Arabic language learners by building a classifier using supervised machine learning methods, achieving an overall accuracy of 87.14% over four complexity classes with SVM and Multinomial Naive Bayes as the most accurate models.
This paper presents an approach based on supervised machine learning methods to build a classifier that can identify text complexity in order to present Arabic language learners with texts suitable to their levels. The approach is based on machine learning classification methods to discriminate between the different levels of difficulty in reading and understanding a text. Several models were trained on a large corpus mined from online Arabic websites and manually annotated. The model uses both Count and TF-IDF representations and applies five machine learning algorithms; Multinomial Naive Bayes, Bernoulli Naive Bayes, Logistic Regression, Support Vector Machine and Random Forest, using unigrams and bigrams features. With the goal of extracting the text complexity, the problem is usually addressed by formulating the level identification as a classification task. Experimental results showed that n-gram features could be indicative of the reading level of a text and could substantially improve performance, and showed that SVM and Multinomial Naive Bayes are the most accurate in predicting the complexity level. Best results were achieved using TF-IDF Vectors trained by a combination of word-based unigrams and bigrams with an overall accuracy of 87.14% over four classes of complexity.