Using autoencoders for training natural language text classifiers

Abstract

Systems and methods for using autoencoders for training natural language classifiers. An example method comprises: producing, by a computer system, a plurality of feature vectors, wherein each feature vector represents a natural language text of a text corpus, wherein the text corpus comprises a first plurality of annotated natural language texts and a second plurality of un-annotated natural language texts; training, using the plurality of feature vectors, an autoencoder represented by an artificial neural network; producing, by the autoencoder, an output of the hidden layer, by processing a training data set comprising the first plurality of annotated natural language texts; and training, using the training data set, a text classifier that accepts an input vector comprising the output of the hidden layer and yields a degree of association, with a certain text category, of a natural language text utilized to produce the output of the hidden layer.