An Artificial Intelligence Method for Automatic Assessment of Fuzzy Semantics in English Literature

Abstract

This Online writing and evaluation are becoming increasingly popular, as is automatic literature assessment. The most popular way is to obtain a good evaluation of the essay and article is by the automatic scoring model. However, assessing fuzzy semantics contained in reports and papers takes much work. An automated essay and articles assessment model using the long-short-term memory (LSTM) neural network is developed and validated to obtain an appropriate assessment. The relevant theoretical basis of the recurrent neural network is introduced first, and the quadratic weighted kappa (QWK) elevation method is cited here to develop the model. The LSTM network is then awarded for developing the general automatic assessment model. The available model is modified to get better performance by adding a convolutional layer(s). Finally, a data set of 7000 essays is segmented based on the ratio of 6:2:2 to train, validate, and test the model. The results indicate that the LSTM network can effectively capture the general properties of the essay and articles. After adding the convolutional layer(s), the LSTM+convolutional layer(s) model can get better performance. The QWK values are higher than 0.6 and have an improvement of 0.097 to 0.134 compared with the LSTM network, which proves that the results of the LSTM network combined with the convolutional layer(s) model are overall satisfactory, and the modified model has practical values.