A Graph-based Readability Assessment Method using Word Coupling

Zhiwei Jiang, Gang Sun, Qing Gu, Tao Bai, Daoxu Chen

Abstract: This paper proposes a graph-based readability assessment method using word coupling. Compared to the state-of-theart methods such as the readability formulae, the word-based and feature-based methods, our method develops a coupled bag-of-words model which combines the merits of word frequencies and text features. Unlike the general bag-of-words model which assumes words are independent, our model correlates the words based on their similarities on readability. By applying TF-IDF (Term Frequency and Inverse Document Frequency), the coupled TF-IDF matrix is built, and used in the graph-based classification framework, which involves graph building, merging and label propagation. Experiments are conducted on both English and Chinese datasets. The results demonstrate both effectiveness and potential of the method.