Large-scale Multi-label Text Classification - Revisiting Neural Networks

Large-scale datasets with multi-labels are becoming readily available, and the demand for large-scale multi-label classification algorithm is also increasing. In this work, we propose to utilize a single-layer Neural Networks approach in large-scale multi-label text classification tasks with recently proposed learning techniques. We carried out experiments on six textual datasets with varying characteristics and size, and show that a simple Neural Networks model equipped with recent advanced techniques for Neural Networks components such as activation layer, optimization, and generalization algorithms performs as well as or even outperforms the previous state-of-the-art approaches on large-scale datasets with diverse characteristics.