Exploiting Morphological, Grammatical, and Semantic Correlates for Improved Text Difficulty Assessment

Abstract

We present a low-resource, language-independent system for text difficulty assessment. We replicate and improve upon a baseline by Shen et al. (2013) on the Interagency Language Roundtable (ILR) scale. Our work demonstrates that the addition of morphological, information theoretic, and language modeling features to a traditional readability baseline greatly benefits our performance. We use the Margin-Infused Relaxed Algorithm and Support Vector Machines for experiments on Arabic, Dari, English, and Pashto, and provide a detailed analysis of our results.