| With the development of the information age,the text data carrying rich information is growing exponentially,and it is a hot and difficult research task to classify the complex text information and extract the effective information for the use of the society.Neural network methods are widely used in text classification tasks because of their powerful feature representation and large-scale data processing capability,but there are still some shortcomings.For example,convolutional neural networks focus on extracting local features of text,but it is difficult to handle long text structure information and capture contextual semantic relationships;recurrent neural networks focus on extracting long distance information of text,but there is a short-term memory problem and it is very easy to cause gradient disappearance.It can be seen that a single neural network cannot fully extract text features,which leads to unsatisfactory classification results.To address the above problems,this paper researches the text representation and feature extraction techniques in text classification,and proposes a hybrid neural network-based text classification model.The main research contents are as follows:(1)A hybrid neural network model(SAR-BC)based on self-attentive mechanism and residual network is proposed to address the problem of inadequate feature extraction of single neural network model.The model first extracts the contextual semantic features and local features of the input text through Bi GRU-CNN,then uses the self-attentive mechanism to assign weights to the convolutional information to filter out the key text features,and finally introduces the residual network after normalizing the pooled feature information layer to solve the problem of gradient disappearance and network degradation in deep neural networks.In addition,a smoother Mish function is used as the nonlinear activation function during the model operation to improve the generalization ability of the model.The performance of the hybrid neural network model based on the self-attentive mechanism and the residual network is better than other comparative models,which confirms that the model can extract more comprehensive text features and thus improve the classification performance.(2)To address the problems that Chinese text has too single vector representation and low classification accuracy,this paper proposes a hybrid neural network model based on multi-feature fusion on the basis of Chapter 3.The model first uses Word2 vec to obtain the original semantic vectors at the word level,and then extends the features with lexical features,pinyin character features and five-stroke character features to reduce the impact of homophones and misspellings in Chinese texts.The processed feature representations are then input to the SAR-BC model for both global and local feature extraction,and finally the output multiple semantic features are fused to enrich the semantic representation of the text and input to the fully connected layer to complete the classification.Through comparison experiments and ablation experiments on two Chinese datasets,the performance indexes of the hybrid neural network model based on multi-feature fusion constructed in this paper are better than those of other comparison models in the experiments,confirming that the model can obtain richer Chinese feature representations and thus improve the model performance. |
