关键词: 动态多标签, 文本分类, 神经网络, 标签语义相似

Abstract:

To solve the problem of dynamic multi-label text classification with time-varying labels, a dynamic multi label text classification algorithm based on label semantic similarity is proposed. In the training phase, a multi-label text classifier based on convolutional neural network is trained, and then the output of the penultimate layer of the classifier is taken as the feature vector of the text. Because the feature vector is trained with labels, it contains label semantic information compared with the content-based feature vector. In the test phase, the test document is input into the multi label text classifier in the training phase to obtain the corresponding feature vector, and then the cosine similarity is calculated. At the same time, a time attenuation factor is added to make the recent text have a higher similarity value. Finally, the nearest neighbor algorithm is used for classification. The experimental results show that the proposed algorithm has better performance in dealing with dynamic multi-label text classification problem.

Key words: dynamic multi-label, text classification, neural networks, label semantic similarity