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
表2 不同级别下的Top30特征词
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
表3 筛选不同结构特征后的性能比较
图4 初中和高中文本的主题分布与相应主题词
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
表4 不同超参数对网络性能的影响
图5 单类型特征在多分类器下的比较结果
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
表5 多元特征融合实验结果
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
表6 与现有方法的比较结果
图6 文本分级识别结果
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