Ensemble application of convolutional and recurrent neural networks for multi-label text categorization

Text categorization, or text classification, is one of key tasks for representing the semantic information of documents. Multi-label text categorization is finer-grained approach to text categorization which consists of assigning multiple target labels to documents. It is more challenging compared to the task of multi-class text categorization due to the exponential growth of label combinations. Existing approaches to multi-label text categorization fall short to extract local semantic information and to model label correlations. In this paper, we propose an ensemble application of convolutional and recurrent neural networks to capture both the global and the local textual semantics and to model high-order label correlations while having a tractable computational complexity. Extensive experiments show that our approach achieves the state-of-the-art performance when the CNN-RNN model is trained using a large-sized dataset.