Looking at another approach – CNNs
CNNs are very popular for image recognition tasks, but they are less often used for NLP tasks than RNNs because they don’t take into account the temporal order of items in the input. However, they can be useful for document classification tasks. As you will recall from earlier chapters, the representations that are often used in classification depend only on the words that occur in the document—BoW and TF-IDF, for example—so, effective classification can often be accomplished without taking word order into account.
To classify documents with CNNs, we can represent a text as an array of vectors, where each word is mapped to a vector in a space made up of the full vocabulary. We can use word2vec, which we discussed in Chapter 7, to represent word vectors. Training a CNN for text classification with Keras is very similar to the training process that we worked through in MLP classification. We create a sequential model as...
