CNN models for readability of Chinese texts

Han Feng; Sizai Hou; Le-Yin Wei; Ding-Xuan Zhou

doi:10.3934/mfc.2022021

Article Contents

2022, Volume 5, Issue 4: 351-362. Doi: 10.3934/mfc.2022021

This issue Previous Article A generalized projection iterative method for solving non-singular linear systems Next Article

CNN models for readability of Chinese texts

1.
Department of Mathematics, City University of Hong Kong, Hong Kong
2.
The Hong Kong University of Science and Technology (Guangzhou), Nansha, Guangzhou, 511400, Guangdong, China
3.
Shaoxing University, Shaoxing 312000, Zhejiang, China
4.
School of Data Science, Department of Mathematics, Liu Bie Ju Centre for Mathematical Sciences, City University of Hong Kong, Hong Kong

^*Corresponding author: Le-Yin Wei
^*Corresponding author: Le-Yin Wei

Received: June 2022

Early access: July 2022

Published: November 2022

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Abstract

Readability of Chinese texts considered in this paper is a multi-class classification problem with $ 12 $ grade classes corresponding to $ 6 $ grades in primary schools, $ 3 $ grades in middle schools, and $ 3 $ grades in high schools. A special property of this problem is the strong ambiguity in determining the grades. To overcome the difficulty, a measurement of readability assessment methods used empirically in practice is adjacent accuracy in addition to exact accuracy. In this paper we give mathematical definitions of these concepts in a learning theory framework and compare these two quantities in terms of the ambiguity level of texts. A deep learning algorithm is proposed for readability of Chinese texts, based on convolutional neural networks and a pre-trained BERT model for vector representations of Chinese characters. The proposed CNN model can extract sentence and text features by convolutions of sentence representations with filters and is efficient for readability assessment, which is demonstrated with some numerical experiments.

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Mathematics Subject Classification: Primary: 68T07, 68T50; Secondary: 68Q32.

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Figure 1. One Filter Instance

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Figure 2. Two Inputs & Two Filters

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Figure 3. Accuracy Curve by Epoch Number

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Figure 4. Confusion Matrix

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Figure 5. Scatter Plot

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Table 1. Number of Texts in Each Grade

Grade	1	2	3	4	5	6	7	8	9	10	11	12	Total
Texts	235	320	386	321	281	252	145	58	134	86	26	109	2353

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Table 2. Empirical accuracies of various models

Model	Vec2Read[12]	Tseng et al.[12]	Basic	Multi-Channel	Top-$ k $	Fused
$ \hat{{\mathcal A}} $	29.18	29.00	43.9	44.8	45.6	48.6
$ \hat{{\mathcal A}}_{\mathcal C} $	69.70	67.05	83.7	84.3	83.7	88.0

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