Research on automatic grading model of HSK reading texts based on supervised learning

Abstract

Aiming at the problem that there are few effective reference standards and analysis tools available in classifying and grading Hanyu Shuiping Kaoshi(HSK) reading materials, with HSK reading texts in the past years as study object, the text readability features were extracted, and nine supervised learning algorithms, such as support vector machine, decision tree and extreme gradient enhancement, etc., were employed to build a model that could automatically classify self-selected text to the corresponding HSK level. Multiple indicators such as accuracy and AUC were adopted to evaluate the grading effect of each model, and the best model was chosen to design an online tool. The results show that supervised learning has high performance in analyzing and grading HSK reading materials. Among the nine supervised learning models, extreme gradient enhancement is the best, with an accuracy of 0.913 and an AUC of 0.994. The grading model and online tool can grade HSK self-selected texts with high accuracy, help users select texts pertinently and improve learning efficiency.

Keywords

• natural language processing; supervised learning; hsk reading text; readability feature; grading model