Introduction
At the core of AI’s ability to understand language lies an invisible map — one that doesn’t just list words but captures their meaning and connections. How can machines tell that king and queen are related, or that dog is closer to cat than car? The secret lies in word embeddings — the mathematical blueprint that transforms words into something machines can grasp.
In this article, we’ll dive into the world of word embeddings, uncovering how they enable AI to recognize patterns, understand context, and make sense of human language. Whether you're fascinated by the inner workings of AI or just curious about the tech behind your favorite apps, this exploration of word embeddings will show you how machines learn to speak our language.
Let’s decode the hidden language of AI — one word at a time.
Word Embeddings
Word embeddings act as a bridge between human language and machine understanding. They transform words and text into numerical representations, allowing computers to process and interpret language. Each word is encoded as a unique vector of numbers, capturing its meaning in a form that machines can work with.
The image above displays the positions of various items, such as bananas, basketballs, houses, buses, and cars, on a two-dimensional grid. The x-axis runs horizontally, while the y-axis runs vertically. Items with similar characteristics are clustered closely together.
Suppose we want to add a new item, like a carrot, to the grid at one of the possible positions marked (a, b, c, d). Which position would be the most suitable?
Word embeddings work similarly by mapping items (or words) onto a plane based on their similarities. In the image, vehicles are positioned in the lower right corner, while sports-related items are in the upper left. This clustering highlights a key principle of embeddings — items (or words) with similar meanings tend to appear near each other.
Since a carrot is a type of fruit/vegetable, it is most likely to be placed near other fruits. The best position for the carrot is “C” at coordinate [5, 5], where other fruits are located.
Beyond grouping similar words, word embeddings capture the unique features and relationships between words. Just as numbers relate to one another through mathematical operations like addition and subtraction, word embeddings can represent and reflect relationships between words in a language, revealing deeper patterns and connections.
The image above illustrates how words are positioned based on their relationships and proximity to one another. For example, Dog is a larger and more mature form of Puppy, reflecting growth along the size dimension.
Now, imagine we want to place the word Cow on this Cartesian plane, choosing between points A, B, or C. Since the embedding has learned the relationship between Puppy and Dog, it will place Cow at position C with coordinates [3, 4]. This reflects the idea that Cow is a larger and more mature version of Calf, mirroring the vertical growth pattern seen between Puppy and Dog.
This layout reveals two key patterns:
The x-axis represents growth in age or maturity.
The y-axis represents growth in size.
Conclusion
Word embeddings are more than just a technical tool — they represent a fundamental shift in how machines understand and process language. By mapping words to numerical vectors based on their meanings and relationships, embeddings allow AI models to capture the subtleties of language, recognize patterns, and make intelligent associations.
From distinguishing between puppies and dogs to understanding the relationship between calves and cows, embeddings create a rich, multi-dimensional space where words are not just symbols but reflections of their context and features. This ability to translate human language into structured data is what powers modern advancements in search engines, translation models, and conversational AI.
As AI continues to evolve, word embeddings will remain a cornerstone of natural language processing — a reminder that even the simplest words can reveal deep, underlying connections in the vast landscape of language.
References
Cohere, "What Are Word and Sentence Embeddings?," LLM University. [Online]. Available: https://cohere.com/llmu/sentence-word-embeddings