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- W212464659 abstract "In this paper, we introduce a new baseline for language-independent text difficulty assessment applied to the Interagency Language Roundtable (ILR) proficiency scale. We demonstrate that reading level assessment is a discriminative problem 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 error. We provide an analysis of which features are most predictive of a given level." @default.
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- W212464659 date "2013-08-01" @default.
- W212464659 modified "2023-10-18" @default.
- W212464659 title "A Language-Independent Approach to Automatic Text Difficulty Assessment for Second-Language Learners" @default.
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- W212464659 doi "https://doi.org/10.21236/ada595522" @default.
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