  We propose a novel framework to understand the text by converting sentences
or articles into video-like 3-dimensional tensors. Each frame, corresponding to
a slice of the tensor, is a word image that is rendered by the word's shape.
The length of the tensor equals to the number of words in the sentence or
article. The proposed transformation from the text to a 3-dimensional tensor
makes it very convenient to implement an $n$-gram model with convolutional
neural networks for text analysis. Concretely, we impose a 3-dimensional
convolutional kernel on the 3-dimensional text tensor. The first two dimensions
of the convolutional kernel size equal the size of the word image and the last
dimension of the kernel size is $n$. That is, every time when we slide the
3-dimensional kernel over a word sequence, the convolution covers $n$ word
images and outputs a scalar. By iterating this process continuously for each
$n$-gram along with the sentence or article with multiple kernels, we obtain a
2-dimensional feature map. A subsequent 1-dimensional max-over-time pooling is
applied to this feature map, and three fully-connected layers are used for
conducting text classification finally. Experiments of several text
classification datasets demonstrate surprisingly superior performances using
the proposed model in comparison with existing methods.
