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Readability of Spanish e-government information
The present work proposes the study of automated readability assessment, and the different linguistic features that are responsible for the better text comprehensibility of Spanish Government websites of administrative procedures. To fulfil this task, a corpus made by web documents with different difficulty levels has been gathered. Then, these documents' difficulty is assessed through different classic readability metrics. By the use of machine learning methods, different algorithms are analyzed to measure their capability to predict text difficulty. The results obtained show that the official Spanish Government websites have a high difficulty level. The main contribution of this work is the combined application of a wide number of linguistic attributes and the construction of a new corpus addressed to official government texts.