This demo paper introduces the online Readability Leveled Arabic Thesaurus interface. For a given user input word, this interface provides the word’s possible lemmas, roots, English glosses, related Arabic words and phrases, and readability on a five-level readability scale. This interface builds on and connects multiple existing Arabic resources and processing tools. This one-of-a-kind system enables Arabic speakers and learners to benefit from advances in Arabic computational linguistics technologies. Feedback from users of the system will help the developers to identify lexical coverage gaps and errors. A live link to the demo is available at : http://samer.camel-lab.com/.

Advances in automatic readability assessment can impact the way people consume information in a number of domains. Arabic, being a low-resource and morphologically complex language, presents numerous challenges to the task of automatic readability assessment. In this paper, we present the largest and most in-depth computational readability study for Arabic to date. We study a large set of features with varying depths, from shallow words to syntactic trees, for both L1 and L2 readability tasks. Our best L1 readability accuracy result is 94.8 % (75 % error reduction from a commonly used baseline). The comparable results for L2 are 72.4 % (45 % error reduction). We also demonstrate the added value of leveraging L1 features for L2 readability prediction.