PENDING
Please predict whether this patent is acceptable.PATENT ABSTRACT: According to an aspect a method includes configuring a convolutional neural network (CNN) for classifying text based on word embedding features into a predefined set of classes identified by class labels. The predefined set of classes includes a class labeled none-of-the-above for text that does not fit into any of the other classes in the predefined set of classes. The CNN is trained based on a set of training data. The training includes learning parameters of class distributed vector representations (DVRs) of each of the predefined set of classes. The learning includes minimizing a pair-wise ranking loss function over the set of training data. A class embedding matrix of the class DVRs of the predefined set of classes that excludes a class embedding for the none-of-the-above class is generated. Each column in the class embedding matrix corresponds to one of the predefined classes.