Introducing NLP networks
In the previous chapters, we saw how different architectures, such as Multi-Layer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs), deal with numerical data and images, respectively. In this recipe, we will analyze the most important architectures to process natural language expressed as text data.
The most important characteristic of natural language is that it is a list of words of variable length, and the order of those words matters; it is a sequence. The previous architectures that we have analyzed are not suited for variable-length data inputs and also do not exploit the relationships among words effectively.
In this recipe, we will introduce neural networks that have been developed to process sequences of words:
- We will start by applying the network introduced in the previous chapter, that is, CNNs for text processing, called TextCNNs.
- Afterward, we will introduce Recurrent Neural Networks (RNNs) and their vanilla implementation...
