关键词: 文本分类, 词共现, 图卷积神经网络

Abstract:

Aiming at the problem of small number of labels in text classification tasks, a semi-supervised text classification method based on the combination of word co-occurrence and graph convolutional neural networks is proposed. The model uses the word co-occurrence method to count the word co-occurrence information of the words in the corpus, and filters the word co-occurrence information to build a text graph containing a large graph structure of word nodes and document nodes. The feature matrix is input to a graph convolutional neural network combined with attention mechanism to implement text classification. The experimental results show that compared with current multiple text categorization algorithms, this method has achieved better results on the classic data sets 20NG, Ohsumed and MR.

Key words: text categorization, word co-occurrence, Graph Convolutional Network（GCN）