ML-Net: multi-label classification of biomedical texts with deep neural networks

Abstract

ML-Net is able to accuractely represent biomedical document context and dynamically estimate the label count in a more systematic and accurate manner. Unlike traditional machine learning methods, ML-Net does not require human effort for feature engineering and is a highly efficient and scalable approach to tasks with a large set of labels, so there is no need to build individual classifiers for each separate label.