Term-based pooling in convolutional neural networks for text classification

To achieve good results in convolutional neural networks (CNN) for text classification task, term-based pooling operation in CNNs is proposed. Firstly, the convolution results of several convolution kernels are combined by this method, and then the results after combination are made pooling operation, three sorts of CNN models (we named TB-CNN, MCT-CNN and MMCT-CNN respectively) are constructed and then corresponding algorithmic thought are detailed on this basis. Secondly, relevant experiments and analyses are respectively designed to show the effects of three key parameters (convolution kernel, combination kernel number and word embedding) on three kinds of CNN models and to further demonstrate the effect of the models proposed. The experimental results show that compared with the traditional method of text classification in CNNs, term-based pooling method is addressed that not only the availability of the way is proved, but also the performance shows good superiority.