Tibetan text classification based on graph convolutional network

Abstract

In this study of Tibetan text classification based on graph convolutional networks, the text graph is first constructed for the Tibetan news corpus based on word-word relations and word-document relations using the constructive relations of Tibetan words and documents, then initialized using the unique thermal representations of words and documents. The embeddings of words and documents are learned using graph convolutional networks under the supervision of the training set of documents, and finally, the text classification problem is transformed into a node classification problem. The graph convolutional network achieves an accuracy of 68.45 % on the Tibetan text classification task, which is higher than other baseline models. In this paper, two comparative experiments are designed to illustrate the effectiveness of graph convolutional networks on Tibetan text classification, which helps people to solve the problem of confusing information in Tibetan texts.