Katarina Heimann Mühlenbock

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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 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)
A corpus of easy-to-read texts in combination with a base vocabulary pool for Swedish was used in order to build a basic vocabulary. The coverage of these entries by symbols in an existing AAC database was then assessed. We finally suggest a method for enriching the expressive power of the AAC language by combining existing symbols and in this way(More)
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