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Studies have shown that modern methods of readability assessment, using automated linguistic analysis and machine learning (ML), is a viable road forward for readability classification and ranking. In this paper we present a study of different levels of analysis and a large number of features and how they affect an ML-system's accuracy when it comes to(More)
We report results from ongoing research on developing sophisticated measures for assessing a student's reading ability and a tool for the student and teacher to create a profile of this ability. In the project we will also investigate how these measures can be transformed to values on known criteria like vocabulary, grammatical fluency and so forth, and how(More)
We report on results from using the multi-variate readability model SVIT to classify texts into various levels. We investigate how the language features integrated in the SVIT model can be transformed to values on known criteria like vocabulary, grammatical fluency and propositional knowledge. Such text criteria, sensitive to content , readability and genre(More)
Data driven approaches to readability analysis for languages other than English has been plagued by a scarcity of suitable corpora. Often, relevant corpora consist only of easy-to-read texts with no rank information or empirical readability scores, making only binary approaches, such as classification, applicable. We propose a Bayesian, latent variable,(More)
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