Chinese teaching material readability assessment with contextual information
Hao Liu, Si Li, Jianbo Zhao, Zuyi Bao, Xiaopeng Bai · 2017
Readability of an article indicates its level in terms of reading comprehension in general. Readability assessment is a process that measures the reading level of a piece of text, which can help in finding reading materials suitable for readers. In this paper, we aim to evaluate the readability about the Chinese teaching material aimed at second language (L2) learners. We introduce the neural network models to the readability assessment task for the first time. In order to capture the contextual information for readability assessment, we employ Convolutional Neural Network (CNN) to capture hidden local features. Then we use bi-directional Long Short-Term Memory Networks (bi-LSTM) neural network to combine the past and future information together. Experiment results show that our model achieves competitive performance.