Performance analysis of a novel hybrid deep learning approach in classification of quality-related English text

Spatio-Temporal Relevance Classification from Geographic Texts Using Deep Learning

“…They predicted word vectors by masking target words and learned the relationships between words using a self-attention mechanism, thus better incorporating sentence-level semantic information. Lai et al [30] proposed a recurrent convolutional neural network classification method, which maximizes the capture of contextual information by drawing on the common advantages of RNN and CNN, greatly improves the accuracy of the classification ground, and makes the classification of the best; Orosoo et al [31] proposed a CNN based on a typical English text where it is easy to feature fuzzy elements and other shortcomings, to improve the classification accuracy and adaptability; Kong et al [32], based on the data of 12,345 government hotlines in Zhejiang Province from 2017 to 2021, constructed a fine-grained three-level classification system of livelihood issues from the perspective of residents, and utilized the BERT pretraining model to construct a text classification model, which transformed the text of the residents' demands into the labels of livelihood issues; Wang et al [33] proposed a Chinese short-text classification model based on the ERNIE-RCNN model for the difficulties of few feature words, poor normality, and a large amount of the data size in Chinese short text. Li et al [34] proposed a microblog rumor detection model based on the BERT-RCNN model in response to the traditional rumor detection model that requires a large number of features as well as the difficulty in achieving timely detection, and verified the effectiveness of the model.…”