PageCNNs: Convolutional Neural Networks for Multi-label Chinese Webpage Classification with Multi-information Fusion

Along with the popularity and development of the Internet in China, Chinese webpage classification has become an important research topic. As the webpage text is a kind of text, webpage classification is constructed based on text classification. But due the particularity of the webpage composition, the external linked webpages can leverage helpful information to improve the webpage classification performance. The goal of this work is to design accurate multi-label Chinese webpage classification models by effectively fusing the information extracted from current webpage and external linked webpages, including the text information and label information of external linked webpages. A convolutional neural network for webpage classification (PageCNN) model and its two variants (PageCNN-CLL and PageCNN-WLL) are proposed to effectively fuse the text and label information extracted from multiple Chinese webpages. The proposed PageCNN models are compared with two modified traditional machine learning models, the modified TextCNN model, and three state-of-the-art deep learning based multi-label text classification models. The experimental results demonstrate that the PageCNN models perform better than the compared models in terms of subset accuracy, Hamming loss, macro F1, and micro F1. Moreover, the in-depth analysis of the effectiveness of the external linked webpages on current webpage classification is conducted by analyzing the error correction rate and hit rate of the proposed models and preliminary prediction variables. As demonstrated in the experiments, the multi-information fusion methods developed in the PageCNN models can effectively manipulate the input data from multiple webpages to enhance the multi-label Chinese webpage classification performance.