Graph Convolution Word Embedding and Attention for Text Classification∗
Yang Yi, Qihui Cui, Lijun Ji, Zhuoran Cheng · 2022
Text classification is an important and classic task of natural language processing. Deep neural networks are becoming more and more popular in text classification due to their expressive power and low requirements for feature engineering. However, the more flexible graph convolutional neural network is rarely used for this task. This paper proposes a text classification model (GENET) based on graph convolution word embedding and attention mechanism. The model can better combine the semantic and lexical information of the text with the discontinuous global word co-occurrence information and long-distance semantic information in the corpus. It breaks the shortcomings of traditional neural networks that have limited structure and can only learn local information. Experimental results show that our proposed text classification method out performs other models on multiple datasets. At the same time, it is proved through experiments that the word global information obtained by GCN is an important supplement to the word embedding representation.