Frontiers in Educational Research, 2022, 5(6); doi: 10.25236/FER.2022.050618.
Hang Xu1, Yi Yu2, Shuai Li1
1Nanchang Institute of Technology, Nanchang, Jiangxi, China
2Macau University of Science and Technology, Macau, China
English text difficulty classification is very important in all walks of life, so it is very important to determine the text complexity of an article quickly and accurately. We select volumes 1 to 4 of New English Concept as a sample, in order to make up for the lack of terminology and applicability in the New Concept textbook, reader’s Digest USA, Time and Nature, CET-4, CET-6 and IELTS are selected as the supplementary samples. The LCA method and L2SCA method were used to extract 33 lexical-related and 23 syntactic-related indicators from the sample articles as the original data set. The data were input into the artificial neural network for training and cross-validation, the training effect was excellent. After the above processing, the remaining five kinds of sample data were substituted into the trained artificial neural network for text complexity assessment. The standardized reading difficulty coefficients obtained from the assessment were drawn and the results of all kinds of data were found to be in line with the recognized difficulty ranking.
Artificial Neural Network; Hierarchical Clustering
Hang Xu, Yi Yu, Shuai Li. Research on English Text Difficult Auditing Based on Artificial Neural Network. Frontiers in Educational Research (2022) Vol. 5, Issue 6: 93-96. https://doi.org/10.25236/FER.2022.050618.
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