关键词: 卷积神经网络, 图卷积网络, 损失权重, PU文本分类

Abstract:

Towards PU（Positive and Unlabeled） text classification, a PU text classification algorithm based on graph convolution network is proposed. The basic idea is to assign different weights to unlabeled examples. Firstly, the unlabeled examples are all regarded as negative examples to train the text classifier based on convolutional neural network. Then, the vector of the penultimate layer of the convolutional neural network is taken as the feature vector of the text, and the corresponding class probability, as an input to the graph convolutional network. Finally, the loss weight of each unlabeled examples is calculated using the class probability derived from the graph convolutional network, and the text classifier is retrained. It repeats the above three steps until the algorithm parameters are stable. The experimental results on the public dataset 20newsgroup show that the proposed algorithm is superior to the existing ones, especially in the case of fewer positive samples.

Key words: convolutional neural network, graph convolutional network, loss weight, PU text classification