基于多元语言特征与深度特征融合的中文文本阅读难度自动分级研究

Automatic Grading of Chinese Text Reading Difficulty Based on Multiple Linguistic Features and Deep Features

文本阅读难度自动分级是让计算机能够根据文本特征自动判断文本所属的难度级别,该文以此为目标,提出一种基于多元语言特征与深度特征相融合的方法来实现对文本难度的自动分级。其中多元语言特征考虑了汉字、词汇、句子等不同的语言层面,同时涉及到频率、长度、复杂度、丰富度、连贯度等不同维度的信息。另一方面,该文利用了基于BERT的神经网络预训练模型来提取文本中句子的深度特征,在此基础上构建了一个端到端神经网络来将语言特征与深度特征进行融合,最终在自动分级任务上取得了不错的效果,分级正确率超过了基于传统语言特征的方法和基于主流神经网络的方法,充分表明了所提出的特征融合方法在文本阅读难度自动分级任务上的有效性。

Automatic grading of text reading difficulty is to automatically judge the difficulty level according to text features. In this paper, we propose a novel difficulty grading method based on multi-linguistic features and deep features. In this method, various linguistic features are taken into account from characters level, vocabulary level and sentences level, in terms of frequency, length, complexity, richness and coherence. On the other hand, this paper uses the BERT-based pre-trained neural network model to extract the deep features of text sentences. On this basis, an end-to-end neural network is constructed to fuse the multi-linguistic features and deep features. Our methods achieve good performance in automatic grading, outperforming the methods based on traditional linguistic features and on popular neural networks.