Using word embeddings for spam detection

Because of the widespread availability of various robust embeddings generated from large corpora, it has become quite common to use one of these embeddings to convert text input for use with machine learning models. Text is treated as a sequence of tokens. The embedding provides a dense fixed dimension vector for each token. Each token is replaced with its vector, and this converts the sequence of text into a matrix of examples, each of which has a fixed number of features corresponding to the dimensionality of the embedding.

This matrix of examples can be used directly as input to standard (non-neural network based) machine learning programs, but since this book is about deep learning and TensorFlow, we will demonstrate its use with a one-dimensional version of the Convolutional Neural Network (CNN) that you learned about in Chapter 3, Convolutional Neural Networks. Our example is a spam detector that will classify Short Message Service...