A Readability Checker with Supervised Learning using Deep Syntactic and Semantic Indicators

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 in 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 quite comparable results to most surface-based indicators. Finally, a graphical user interface has been developed which highlights difficult-to-read text passages, depending on the individual indicator values, and displays a global readability score.