  TextCNN, the convolutional neural network for text, is a useful deep learning
algorithm for sentence classification tasks such as sentiment analysis and
question classification. However, neural networks have long been known as black
boxes because interpreting them is a challenging task. Researchers have
developed several tools to understand a CNN for image classification by deep
visualization, but research about deep TextCNNs is still insufficient. In this
paper, we are trying to understand what a TextCNN learns on two classical NLP
datasets. Our work focuses on functions of different convolutional kernels and
correlations between convolutional kernels.
