Chapter 6: Convolutional Neural Networks for Text Classification

In the previous chapter, we showed how RNNs can be used to provide sentiment classifications for text. However, RNNs are not the only neural network architecture that can be used for NLP classification tasks. Convolutional neural networks (CNNs) are another such architecture.

RNNs rely on sequential modeling, maintain a hidden state, and then step sequentially through the text word by word, updating the state at each iteration. CNNs do not rely on the sequential element of language, but instead try and learn from the text by perceiving each word in the sentence individually and learning its relationship to the words surrounding it within the sentence.

While CNNs are more commonly used for classifying images for the reasons mentioned here, they have been shown to be effective at classifying text as well. While we do perceive text as a sequence, we also know that the meaning of individual words in the sentence depends...