Random subspace-based decision tree classification foreign Chinese difficulty evaluation method

Abstract

The invention discloses a decision tree classification foreign Chinese difficulty evaluation method based on svm and bert model random subspace feature selection. The method comprises the steps: generating 86 statistical features according to the length and readability of an article, carrying out the classification through svm, and obtaining a confidence degree 1; and classifying the encoding features by using svm to obtain a confidence coefficient 2. And fusing the obtained two confidence coefficients to serve as new features, and classifying the features by using a decision tree. And for theencoding feature data, outputting an information result through the-1 layer of the encoding extracted by the BERT model, and then performing average-> max pooling processing to obtain 768-dimensionalfeatures in total without normalization. According to the method, the problems of low efficiency and under-fitting of a traditional algorithm are avoided, and all information is used most reasonably,so that the classification basis is increased, and the effect is remarkable. According to the method, the accuracy of evaluating the difficulty of foreign Chinese is 85.6%.

Description

technical field [0001] The invention belongs to the field of educational informatization, and in particular relates to a method for evaluating difficulty of Chinese as a foreign language based on decision tree classification of random subspace feature selection of SVM and BERT models. Background technique [0002] We all know that reading should be done step by step, from easy to difficult. Too difficult can easily lead to frustration of students' self-confidence and loss of interest in reading. Too simple and low-level repetition is not conducive to the continuous improvement of reading ability, and cannot meet the academic requirements for reading complex texts and conducting related research after entering university. In short, only the difficulty is suitable for the best. With the development of China, China plays an increasingly important role on the international stage, which makes more people have the demand to learn Chinese. Learning Chinese texts is one of the mo...

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Problems solved by technology

[0008] For at least one of the above deficiencies or improvement needs of the prior art, especially due to the complexity of the text classification problem of Chinese learners, when facing the different needs of Chinese learners, the classification standards will change accordingly, Aiming at this task, the present invention proposes a method for assessing the difficulty of Chinese as a foreign language based on Bert model, svm and decision tree feature fusion

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