A GRAPH CONVOLUTIONAL TEXT CLASSIFICATION METHOD WITH SEMANTIC FEATURES
With the advancement of related research in the field of text classification,text classification methods based on deep learning have become one of the important research directions in this field.Due to its powerful feature extraction capabilities,deep learning models have quite superior performance on text classification tasks.However,due to the high dimensionality of text data and the semantic complexity of natural language,the existing deep learning models still need to be further optimized in the extraction of composite semantic information,and their performance has a non-negligible impact on the text classification effect.Therefore,this paper proposes a text classification model LGCN based on LDA and GCN.The model used the LDA model to learn the associated information of documents,words and topics,and used sliding windows and PMI value calculations to obtain the relationship between characters.TF-IDF was used to obtain the connection between words and documents,and a graph constructed in the form of nodes was obtained by fusing rich semantic information.The GCN model was used to learn the semantic information in the graph and classify the document nodes in the graph to complete the text classification task.The experimental results show that on the same data set,the text classification effect of LGCN model is better than that of reference models such as LSTM.
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