Automatically Grading Text Difficulty with Multiple Features
Yong Cheng1(),Dekuan Xu1,Xueqiang Lv2
1(School of Chinese Language and Literature, Ludong University, Yantai 264025, China) 2(School of Computer Science, Beijing University of Information Technology, Beijing 100192, China)
[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.
school likes happy day nice friends teacher morning eat chinese boy english mother play father lot china afternoon beautiful playing girl homework green friend lunch class tv football breakfast sports
高中
life people time university women study author researchers education college social health experience age person business human public company job american language national brain government body technology family scientists
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