An illustrated and intuitive guide to word embeddings
Welcome to Part 2 of our NLP series. If you caught Part 1, you’ll remember that the challenge we’re tackling is translating text into numbers so that we can feed it into our machine learning models or neural networks.
Previously, we explored some basic (and pretty naive) approaches to this, like Bag of Words and TF-IDF. While these methods get the job done, we also saw their limitations — mainly that they don’t capture the deeper meaning of words or the relationships between them.
This is where word embeddings come in. They offer a smarter way to represent text as numbers, capturing not just the words themselves but also their meaning and context.
Let’s break it down with a simple analogy that’ll make this concept super intuitive.
Imagine we want to represent movies as numbers. Take the movie Knives Out as an example.
We can represent a movie numerically by scoring it across different features, such…