In this chapter, we illustrated the steps for developing a convolutional recurrent neural network for author classification based on articles that they have written. Convolutional recurrent neural networks combine the advantages of two networks into one network. On one hand, convolutional networks can capture high-level local features from the data, while, on the other hand, recurrent networks can capture long-term dependencies in the data involving sequences.
First, convolutional recurrent neural networks extract features using a one-dimensional convolutional layer. These extracted features are then passed to the LSTM recurrent layer to obtain hidden long-term dependencies, which are then passed to a fully connected dense layer. This dense layer obtains the probability of the correct classification of each author based on the data in the articles. Although we used a convolutional...
