In the last few years, convolutional neural networks (CNNs), along with recurrent neural networks (RNNs), have become a basic building block in constructing complex deep learning solutions for various NLP, speech, and time series tasks. LeCun first introduced certain basic parts of the CNN frameworks as a general NN framework to solve various high-dimensional data problems in computer vision, speech, and time series. ImageNet applied convolutions to recognize objects in images; by improving substantially on the state of the art, ImageNet revived interest in deep learning and CNNs. Collobert et al. pioneered the application of CNNs to NLP tasks, such as POS tagging, chunking, named entity resolution, and semantic role labeling. Many changes to CNNs, from input representation, number of layers, types of pooling, optimization techniques, and applications to various NLP tasks have been active subjects of research in the last decade.