Checking for readability or simplicity of texts is important for many institutional and individual users. Formulas for approximately measuring text readability have a long tradition. Usually, they exploit surface-oriented indicators like sentence length, word length, word frequency, etc. However, in many cases, this information is not adequate to realistically approximate the cognitive difficulties a person can have to understand a text. Therefore we use deep syntactic and semantic indicators

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... addition. The syntactic information is represented by a dependency tree, the semantic information by a semantic network. Both representations are automatically generated by a deep syntactico-semantic analysis. A global readability score is determined by applying a nearest neighbor algorithm on 3,000 ratings of 300 test persons. The evaluation showed that the deep syntactic and semantic indicators lead to promising results comparable to the best surface-based indicators. The combination of deep and shallow indicators leads to an improvement over shallow indicators alone. Finally, a graphical user interface was developed which highlights difficult passages, depending on the individual indicator values, and displays a global readability score. Povzetek: Strojno učenje z odvisnostnimi drevesi je uporabljeno za ugotavljanje berljivosti besedil.