Construction of Quantitative Index System of Vocabulary Difficulty in Chinese Grade Reading

Abstract

Chinese grade reading for children has a broad application prospect. In this paper, Chinese textbooks for grade 1 to 6 of primary schools published by People’s Education Press are taken as data sets, and the texts are divided into 12 difficulty levels successively. The effective lexical indexes to measure the readability of texts are discussed, and a regression model to effectively measure the lexical difficulty of Chinese texts is established. The study firstly collected 30 indexes at the text lexical level from the three dimensions of lexical richness, semantic transparency and contextual dependence, selected the 7 indexes with the highest relevance to the text difficulty through Person correlation coefficient, and finally constructed a Regression to predict the text difficulty based on Lasso Regression, ElasticNet, Ridge Regression and other algorithms. The regression results show that the model fits well, and the predicted value could explain 89.3% of the total variation of text difficulty, which proves that the quantitative index of vocabulary difficulty of Chinese text constructed in this paper is effective, and can be applied to Chinese grade reading and computer automatic grading of Chinese text difficulty.