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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (7): 103-112    DOI: 10.11925/infotech.2096-3467.2018.1089
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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)
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Abstract  

[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.
Key words： Multiple Features      Reading Difficulty      Automatic Grading     
Received: 30 September 2018      Published: 06 September 2019
ZTFLH:  G353  
Corresponding Authors: Yong Cheng     E-mail: chengokyong@126.com
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Yong Cheng
Dekuan Xu
Xueqiang Lv

Cite this article:

Yong Cheng,Dekuan Xu,Xueqiang Lv. Automatically Grading Text Difficulty with Multiple Features. Data Analysis and Knowledge Discovery, 2019, 3(7): 103-112.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.1089     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2019/V3/I7/103

序号 名称 解释/公式 序号 名称 解释/公式
1 avg_sentence_len[7-8, 22-23] 句子平均长度 9 fun_smog_index[9] $1.043\times \sqrt{polysyllables\times \frac{30}{sentences}}+3.129$
2 avg_syllables[7] 单词的平均音节数 10 fun_kincaid[7] $0.39\times \left( \frac{words}{sentences} \right)+11.8\times \left( \frac{syllables}{words} \right)-15.59$
3 avg_letters[22] 单词的平均字母数 11 fun_readability[22] $4.71\times \left( \frac{letters}{words} \right)+0.5\times \left( \frac{words}{sentences} \right)-21.4$
4 total_polysyllables[9] 多音节的总数 12 fun_coleman_liau[24] $5.88\times \left( \frac{letters}{words} \right)+29.6\times \left( \frac{sentences}{words} \right)-15.8$
5 total_syllables[7] 音节的总数 13 fun_dale_chall[8] $15.8\times \left( \frac{difficult\text{ }words}{words} \right)+0.0496\times \left( \frac{words}{sentences} \right)$
6 total_words[22,23] 总词数 14 fun_flesch[7] $206.8-1.02\times \left( \frac{words}{sentences} \right)-84.6\times \left( \frac{syllables}{words} \right)$
7 total_sentences[9] 句子总数 15 fun_gunning_fog[24] $0.4\times \left( \frac{words}{sentences} \right)+100\times \left( \frac{difficult\text{ }words}{words} \right)$
8 total_difficult_words[23,24] 生僻词总数
级别 特征词
初中 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
序号 筛选特征 分级准确率 序号 筛选特征 分级准确率
13 -fun_dale_chall 0.821 4 -total_polysyllables 0.837
12 -fun_coleman_liau 0.829 14 -fun_flesch 0.838
8 -total_difficult_words 0.830 15 -fun_gunning_fog 0.838
3 -avg_letters 0.834 5 -total_syllables 0.839
11 -fun_readability 0.836 2 -avg_syllables 0.839
9 -fun_smog_index 0.837 10 -fun_kincaid 0.840
7 -total_sentences 0.837 6 -total_words 0.841
1 -avg_sentence_len 0.837 无 0.841
状态向量维度 卷积网络窗口数
维度 分级正确率 数目 分级正确率
32 0.867 2 0.870
64 0.880 3 0.869
128 0.869 4 0.871
192 0.871 5 0.880
256 0.873 6 0.878
特征数目 分级前融合 分级后融合
开发集 测试集 开发集 测试集
单元特征 F 0.850 0.833 0.850 0.833
S 0.843 0.845 0.843 0.845
T 0.815 0.816 0.815 0.816
M 0.880 0.870 0.880 0.870
二元特征
融合		 F&S 0.881 0.874 0.875 0.860
F&T 0.866 0.843 0.846 0.837
F&M 0.886 0.877 0.886 0.878
S&T 0.871 0.856 0.859 0.848
S&M 0.883 0.878 0.886 0.879
T&M 0.882 0.876 0.883 0.873
三元特征
融合		 F&S&T 0.884 0.877 0.874 0.846
F&S&M 0.887 0.878 0.881 0.875
F&T&M 0.885 0.878 0.878 0.873
S&T&M 0.884 0.877 0.884 0.875
四元特征
融合		 F&S&T&M 0.888 0.880 0.888 0.871
对比方法 正确率(校验集) 正确率(测试集)
Random Guess 0.494 0.491
FKGL 0.706 0.709
VM_KNN 0.799 0.780
CNN_SC 0.852 0.863
Our Model 0.888 0.880
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