NLP for machine learning
Unlike humans, computers do not understand text – at least not in the same way that we do. In order to create machine learning models that are able to learn from data, we must first learn to represent natural language in a way that computers are able to process.
When we discussed machine learning fundamentals, you may have noticed that loss functions all deal with numerical data so as to be able to minimize loss. Because of this, we wish to represent our text in a numerical format that can form the basis of our input into a neural network. Here, we will cover a couple of basic ways of numerically representing our data.
Bag-of-words
The first and most simple way of representing text is by using a bag-of-words representation. This method simply counts the words in a given sentence or document and counts all the words. These counts are then transformed into a vector where each element of the vector is the count of the times each word in the...
