''Text classification is the problem of assigning a text document into one or more topic categories or classes. In multiclass document classification as distinguished from binary document classification, there are more than two class. In multilabel classification each document may have more than one class label. For example, given classes N. America, S. America, Europe, Asia and Australia, a news article about U.S. troops in Bosnia may be labeled with both the N. America and Europe classes.''

''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.''

http://arxiv.org/abs/1312.5419