  Readability assessment is the task of determining how difficult or easy a
text is or which level/grade it has. Traditionally, language dependent
readability formula have been used, but these formulae take few text
characteristics into account. However, Natural Language Processing (NLP) tools
that assess the complexity of texts are able to measure more different features
and can be adapted to different languages. In this paper, we present the
MultiAzterTest tool: (i) an open source NLP tool which analyzes texts on over
125 measures of cohesion,language, and readability for English, Spanish and
Basque, but whose architecture is designed to easily adapt other languages;
(ii) readability assessment classifiers that improve the performance of
Coh-Metrix in English, Coh-Metrix-Esp in Spanish and ErreXail in Basque; iii) a
web tool. MultiAzterTest obtains 90.09 % in accuracy when classifying into
three reading levels (elementary, intermediate, and advanced) in English and
95.50 % in Basque and 90 % in Spanish when classifying into two reading levels
(simple and complex) using a SMO classifier. Using cross-lingual features,
MultiAzterTest also obtains competitive results above all in a complex vs
simple distinction.
