Effective Use of Word Order for Text Categorization with Convolutional Neural Networks

Understand

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.