There is a famous saying by German writer Johann Wolfgang von Goethe:

"Tell me with whom you consort and I will tell you who you are..."

Goethe’s assumption is that the people you spend time with each and every day are a reflection of who you are as a person. Would you agree?

Now, what if we extend that same idea from people to words? Linguist John Rupert Firth took this step and came up with his own saying:

"You shall know a word by the company it keeps."

This idea that a word’s meaning can be understood by its context, or the words that surround it, is the basis for word embeddings. A word embedding is a representation of a word as a numeric vector, enabling us to compare and contrast how words are used and identify words that occur in similar contexts.

The applications of word embeddings include:

  • entity recognition in chatbots
  • sentiment analysis
  • syntax parsing

Have little to no experience with vectors? Do not fear! We’ll break down word embeddings and how they relate to vectors, step-by-step, in the next few exercises with the help of the spaCy package. Let’s get started!


In script.py we have loaded the spaCy package and processed a collection of words into word embeddings. We then compare the embeddings for two pairs:

  • "summer" and "winter"
  • "mustard" and "amazing"

What do you notice about the distance between words with similar usages and words with very different usages?

Uncomment some of the print statements at the bottom of the file and run the code to see what a word embedding looks like. Soon you will learn how to load, create, and compare word embeddings just like these!

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