A Language-Independent Approach to Automatic Text Difficulty Assessment for Second-Language Learners
Wade Shen, Jennifer A. Williams, Tamas Marius, Elizabeth Salesky · 2013
In this paper, we introduce a new base-line for language-independent text diffi-culty assessment applied to the Intera-gency Language Roundtable (ILR) profi-ciency scale. We demonstrate that reading level assessment is a discriminative prob-lem that is best-suited for regression. Our baseline uses z-normalized shallow length features and TF-LOG weighted vectors on bag-of-words for Arabic, Dari, English, and Pashto. We compare Support Vector Machines and the Margin-Infused Relaxed Algorithm measured by mean squared er-ror. We provide an analysis of which fea-tures are most predictive of a given level. 1