%A Yong Cheng,Dekuan Xu,Xueqiang Lv %T Automatically Grading Text Difficulty with Multiple Features %0 Journal Article %D 2019 %J Data Analysis and Knowledge Discovery %R 10.11925/infotech.2096-3467.2018.1089 %P 103-112 %V 3 %N 7 %U {https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/abstract/article_4677.shtml} %8 2019-07-25 %X

[Objective] This paper aims to automatically grade reading difficulty of textual documents. [Methods] We used machine learning method based on multiple features of the texts to decide their difficulty levels automatically. The features, which include word-frequency, structures, topics, and depth, describe the textual contents from different perspectives. [Results] We evaluated our method with the reading comprehension texts for high-school English exams, and achieved an accuracy of 0.88. Our result is better than those of the traditional difficulty classification methods. [Limitations] Due to the high cost of manual annotation, the existing datasets cannot be used to improve our method. [Conclusions] The proposed method increased the effectiveness of machine leanring based data analysis.