  Convolutional neural network (CNN) is a neural network that can make use of
the internal structure of data such as the 2D structure of image data. This
paper studies CNN on text categorization to exploit the 1D structure (namely,
word order) of text data for accurate prediction. Instead of using
low-dimensional word vectors as input as is often done, we directly apply CNN
to high-dimensional text data, which leads to directly learning embedding of
small text regions for use in classification. In addition to a straightforward
adaptation of CNN from image to text, a simple but new variation which employs
bag-of-word conversion in the convolution layer is proposed. An extension to
combine multiple convolution layers is also explored for higher accuracy. The
experiments demonstrate the effectiveness of our approach in comparison with
state-of-the-art methods.
