Computers don’t understand words. They understand numbers.

So the big question is:

How do we convert words into numbers without losing meaning?

The answer is:

Word Embeddings

What are Word Embeddings?

Word Embeddings are numerical representations of words that capture their meaning, so that similar words have similar numbers.

In simple language:

Word embeddings convert words into meaningful numbers (vectors).

Not random numbers — meaningful numbers.

Why do we need Word Embeddings?

Look at these words:

• good
• excellent
• bad
• terrible

Humans know:

• good ≈ excellent
• bad ≈ terrible

But computers don’t.

Word embeddings help machines understand that:

• good is closer to excellent
• bad is closer to terrible

So instead of treating every word as totally different, embeddings capture relationships between words.

There are two major approaches of word embeddings:

1. Count / Frequency-Based Methods
2. Deep Learning-Based Methods

1️⃣ Count or Frequency-Based Methods (Traditional Approach)

Count or Frequency

These are the early and simple techniques used in NLP. They rely on counting how often words appear.

It contains:

1. One-Hot Encoding (OHE)
2. Bag of Words (BoW)
3. TF-IDF (Term Frequency–Inverse Document Frequency)

2️⃣ Deep Learning-Based Methods (Modern Approach)

This represents the shift from simple counting techniques to neural network-based models that learn word meaning from context.

It contains

Word2Vec

1. CBOW (Continuous Bag of Words)
2. Skip-Gram