Weakly-Supervised Character-Level Convolutional Neural Networks For Text Classification

Text classification is a fundamental task in Natural Language Processing (NLP). In this paper, we propose a Weakly-Supervised Character-level Convolutional Network (WSCCN) for text classification. Compared to the word-based model, WSCCN extracting information from raw signals. Further, through the combination of global pooling and fully convolutional networks, our model retains semantic position information from stem to stern. Extensive experiments on the most widely-used seven large-scale datasets show that WSCCN could not only achieve state-of-the-art or competitive classification results but show critical parts of the text for classification.