Language is one of the most complex forms of communication, and getting machines to understand it is no easy task. Unlike numbers, words have meanings that depend on context, structure, and even culture. Traditional computational models struggle with this complexity, which is why word embeddings (numerical representations of words) have revolutionized Natural Language Processing (NLP).
What is NLP?
Natural Language Processing (NLP) is a field of Artificial Intelligence (AI) that enables machines to understand, interpret, and generate human language. From chatbots and search engines to machine translation and sentiment analysis, NLP powers many real-world applications.
However, for machines to process language, we need to convert words into numerical representations. Unlike humans, computers do not understand words as meaningful entities — they only process numbers. The challenge in NLP is how to represent words numerically while preserving their meaning and relationships.
The Challenge: Why Raw Text Doesn’t Work?
When humans read a sentence like:
“The cat sat on the mat.”
We immediately understand that “cat” and “mat” are nouns, and that the sentence has a simple structure. But for a computer, this sentence is just a sequence of characters or strings. It has no inherent meaning.
One simple solution is to assign numbers to words.
However, this numerical ID approach fails because:
- It doesn’t capture meaning — “cat” and “dog” are similar, but their numerical IDs are arbitrary.
- It doesn’t show relationships — Words with similar meanings should have similar representations.
- It doesn’t scale — A new word would need a completely new ID.
The Need for a Smarter Representation
A better approach is to represent words using vectors in a multi-dimensional space — where words with similar meanings are closer together. This is where word embeddings come in.
Word embeddings are dense vector representations that allow words to be mathematically compared and manipulated. They are the foundation of modern NLP models, enabling applications like:
- Google Search understanding synonyms (e.g., “car” ≈ “automobile”).
- Chatbots & Virtual Assistants understanding user queries.
- Machine Translation (Google Translate) accurately translating words in different languages.
In this article, we will explore the journey from simple text representations to advanced embeddings like Word2Vec, GloVe, FastText, and contextual models like BERT.