  Filters of convolutional networks used in computer vision are often
visualized as image patches that maximize the response of the filter. We use
the same approach to interpret weight matrices in simple architectures for
natural language processing tasks. We interpret a convolutional network for
sentiment classification as word-based rules. Using the rule, we recover the
performance of the original model.
